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The resurgence of Artificial Intelligence (AI) has been accompanied by a rise in ethical issues. AI practitioners either face challenges in making ethical choices when designing AI-based systems or are not aware of such challenges in the first place. Increasing the level of awareness and understanding of the perceptions of those who develop AI systems is a critical step toward mitigating ethical issues in AI development. Motivated by these challenges, needs, and the lack of engaging approaches to address these, we developed an interactive, scenario-based ethical AI quiz. It allows AI practitioners, including software engineers who develop AI systems, to self-assess their awareness and perceptions about AI ethics. The experience of taking the quiz, and the feedback it provides, will help AI practitioners understand the gap areas, and improve their overall ethical practice in everyday development scenarios. To demonstrate these expected outcomes and the relevance of our tool, we also share a preliminary user study. The video demo can be found at https://zenodo.org/record/7601169#.Y9xgA-xBxhF.

Human values such as honesty, social responsibility, fairness, privacy, and the like are things considered important by individuals and society. Software systems, including mobile software applications (apps), may ignore or violate such values, leading to negative effects in various ways for individuals and society. While some works have investigated different aspects of human values in software engineering, this mixed-methods study focuses on honesty as a critical human value. In particular, we studied (i) how to detect honesty violations in mobile apps, (ii) the types of honesty violations in mobile apps, and (iii) the perspectives of app developers on these detected honesty violations. We first develop and evaluate 7 machine learning (ML) models to automatically detect violations of the value of honesty in app reviews from an end-user perspective. The most promising was a Deep Neural Network model with F1 score of 0.921. We then conducted a manual analysis of 401 reviews containing honesty violations and characterised honesty violations in mobile apps into 10 categories: unfair cancellation and refund policies; false advertisements; delusive subscriptions; cheating systems; inaccurate information; unfair fees; no service; deletion of reviews; impersonation; and fraudulent-looking apps. A developer survey and interview study with mobile developers then identified 7 key causes behind honesty violations in mobile apps and 8 strategies to avoid or fix such violations. The findings of our developer study also articulate the negative consequences that honesty violations might bring for businesses, developers, and users. Finally, the app developers’ feedback shows that our prototype ML-based models can have promising benefits in practice.

OBJECTIVE: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. METHODS: Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017-2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). RESULTS: There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. CONCLUSIONS: Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.

Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.

The elicitation of end-users& human values — such as freedom, honesty, transparency, etc — is important in the development of software systems. We carried out two preliminary Q-studies to understand (a) the general human value opinion types of eHealth applications (apps) end-users (b) the eHealth domain human value opinion types of eHealth apps end-users (c) whether there are differences between the general and eHealth domain opinion types. Our early results show three value opinion types using generic value instruments: (1) fun-loving, success-driven and independent end-user, (2) security-conscious, socially-concerned, and success-driven end-user, and (3) benevolent, success-driven, and conformist end-user. Our results also show two value opinion types using domain-specific value instruments: (1) security-conscious, reputable, and honest end-user, and (2) success-driven, reputable and pain-avoiding end-user. Given these results, consideration should be given to domain context in the design and application of values elicitation instruments.

The Coronavirus disease 2019 (COVID-19) outbreak quickly spread around the world, resulting in over 240 million infections and 4 million deaths by Oct 2021. While the virus is spreading from person to person silently, fear has also been spreading around the globe. The COVID-19 information from the Australian Government is convincing but not timely or detailed, and there is much information on social networks with both facts and rumors. As software engineers, we have spontaneously and rapidly constructed a COVID-19 information dashboard aggregating reliable information semi-automatically checked from different sources for providing one-stop information sharing site about the latest status in Australia. Inspired by the John Hopkins University COVID-19 Map, our dashboard contains the case statistics, case distribution, government policy, latest news, with interactive visualization. In this paper, we present a participant’s in-person observations in which the authors acted as founders of https://covid-19-au.com/ serving more than 830K users with 14M page views since March 2020. According to our first-hand experience, we summarize 9 lessons for developers, researchers and instructors. These lessons may inspire the development, research and teaching in software engineer aspects for coping with similar public crises in the future.

Human values such as integrity, privacy, curiosity, security, and honesty are guiding principles for what people consider important in life. Such human values may be violated by mobile software applications (apps), and the negative effects of such human value violations can be seen in various ways in society. In this work, we focus on the human value of honesty. We present a model to support the automatic identification of violations of the value of honesty from app reviews from an end-user perspective. Beyond the automatic detection of honesty violations by apps, we also aim to better understand different categories of honesty violations expressed by users in their app reviews. The result of our manual analysis of our honesty violations dataset shows that honesty violations can be characterised into ten categories: unfair cancellation and refund policies; false advertisements; delusive subscriptions; cheating systems; inaccurate information; unfair fees; no service; deletion of reviews; impersonation; and fraudulent-looking apps. Based on these results, we argue for a conscious effort in developing more honest software artefacts including mobile apps, and the promotion of honesty as a key value in software development practices. Furthermore, we discuss the role of app distribution platforms as enforcers of ethical systems supporting human values, and highlight some proposed next steps for human values in software engineering (SE) research.

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objectives: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusions: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.

Automated test generators, such as search-based software testing (SBST) techniques are primarily guided by coverage information. As a result, they are very effective at achieving high code coverage. However, is high code coverage alone sufficient to detect bugs effectively In this paper, we propose a new SBST technique, predictive many objective sorting algorithm (PreMOSA), which augments coverage information with defect prediction information to decide where to increase the test coverage in the class under test (CUT). Through an experimental evaluation using 420 labelled bugs on the Defects4J benchmark and using theoretical defect predictors, we demonstrate the improved effectiveness and efficiency of PreMOSA in detecting bugs when using any acceptable defect predictor, i.e., a defect predictor with recall and precision 75%, compared to the state-of-the-art dynamic many objective sorting algorithm (DynaMOSA). PreMOSA detects up to 8.3% more labelled bugs on average than DynaMOSA when given a time budget of 2 minutes for test generation per CUT.

Context: Machine learning (ML) software systems are permeating many aspects of our life, such as healthcare, transportation, banking, and recruitment. These systems are trained with data that is often biased, resulting in biased behaviour. To address this issue, fairness testing approaches have been proposed to test ML systems for fairness, which predominantly focus on assessing classification-based ML systems. These methods are not applicable to regression-based systems, for example, they do not quantify the magnitude of the disparity in predicted outcomes, which we identify as important in the context of regression-based ML systems. Method: We conduct this study as design science research. We identify the problem instance in the context of emergency department (ED) wait-time prediction. In this paper, we develop an effective and efficient fairness testing approach to evaluate the fairness of regression-based ML systems. We propose fairness degree, which is a new fairness measure for regression-based ML systems, and a novel search-based fairness testing (SBFT) approach for testing regression-based machine learning systems. We apply the proposed solutions to ED wait-time prediction software. Results: We experimentally evaluate the effectiveness and efficiency of the proposed approach with ML systems trained on real observational data from the healthcare domain. We demonstrate that SBFT significantly outperforms existing fairness testing approaches, with up to 111% and 190% increase in effectiveness and efficiency of SBFT compared to the best performing existing approaches. Conclusion: These findings indicate that our novel fairness measure and the new approach for fairness testing of regression-based ML systems can identify the degree of fairness in predictions, which can help software teams to make data-informed decisions about whether such software systems are ready to deploy. The scientific knowledge gained from our work can be phrased as a technological

Objective: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. Methods: Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017–2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). Results: There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. Conclusions: Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.

Many software engineering tools build and evaluate their models based on historical data to support development and process decisions. These models help us answer numerous interesting questions, but have their own caveats. In a real-life setting, the objective function of human decision-makers for a given task might be influenced by a whole host of factors that stem from their cognitive biases, subverting the ideal objective function required for an optimally functioning system. Relying on this data as ground truth may give rise to systems that end up automating software engineering decisions by mimicking past sub-optimal behaviour. We illustrate this phenomenon and suggest mitigation strategies to raise awareness.

Background: The existing literature in software engineering reports adverse effects of confirmation bias on software testing. Confirmation bias among software testers leads to confirmatory behaviour, which is designing or executing relatively more specification consistent test cases (confirmatory behaviour) than specification inconsistent test cases (disconfirmatory behaviour). Objective: We aim to explore the antecedents to confirmatory and disconfirmatory behaviour of software testers. Furthermore, we aim to understand why and how those antecedents lead to (dis)confirmatory behaviour. Method: We follow grounded theory method for the analyses of the data collected through semi-structured interviews with twelve software testers. Results: We identified twenty antecedents to (dis)confirmatory behaviour, and classified them in nine categories. Experience and Time are the two major categories. Experience is a disconfirmatory category, which also determines which behaviour (confirmatory or disconfirmatory) occurs first among software testers, as an effect of other antecedents. Time Pressure is a confirmatory antecedent of the Time category. It also contributes to the confirmatory effects of antecedents of other categories. Conclusion: The disconfirmatory antecedents, especially that belong to the testing process, e.g., test suite reviews by project team members, may help circumvent the deleterious effects of confirmation bias in software testing. If a team’s resources permit, the designing and execution of a test suite could be divided among the test team members, as different perspectives of testers may help to detect more errors. The results of our study are based on a single context where dedicated testing teams focus on higher levels of testing. The study’s scope does not account for the testing performed by developers. Future work includes exploring other contexts to extend our results.

OBJECTIVE: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. METHOD(S): Twelve emergency departments provided 3years of retrospective administrative data from Australia (2017-2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). RESULT(S): There were 1930609 patient episodes analysed and median site wait times varied from 24 to 54min. Individual site model prediction median absolute errors varied from+/-22.6min (95%CI 22.4 to 22.9) to +/-44.0min (95%CI 43.4 to 44.4). Global model prediction median absolute errors varied from +/-33.9min (95%CI 33.4 to 34.0) to +/-43.8min (95%CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. CONCLUSION(S): Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.Copyright © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.

Study objective: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments. Method(s): Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. Result(s): There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. Conclusion(s): Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.Copyright © 2021 American College of Emergency Physicians

STUDY OBJECTIVE: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments. METHODS: Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. RESULTS: There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. CONCLUSION: Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.

In this paper, we investigate the effect of TDD, as compared to a non-TDD approach, as well as its retainment (or retention) over a time span of (about) six months. To pursue these objectives, we conducted a (quantitative) longitudinal cohort study with 30 novice developers (i.e., third-year undergraduate students in Computer Science). We observed that TDD affects neither the external quality of software products nor developers’ productivity. However, we observed that the participants applying TDD produced significantly more tests, with a higher fault-detection capability, than those using a non-TDD approach. As for the retainment of TDD, we found that TDD is retained by novice developers for at least six months.

Inter-release defect prediction (IRDP) is a practical scenario that employs the datasets of the previous release to build a prediction model and predicts defects for the current release within the same software project. A practical software project experiences several releases where data of each release appears in the form of chunks that arrive in temporal order. The evolving data of each release introduces new concept to the model known as concept drift, which negatively impacts the performance of IRDP models. In this study, we aim to examine and assess the impact of feature selection (FS) on the performance of IRDP models and the robustness of the model to concept drift. We conduct empirical experiments using 36 releases of 10 open-source projects. The Friedman and Nemenyi Post-hoc test results indicate that there were statistical differences between the prediction results with and without FS techniques. IRDP models trained on the data of most recent releases were not always the best models. Furthermore, the prediction models trained with carefully selected features could help reduce concept drifts.

Researcher bias occurs when researchers influence the results of an empirical study based on their expectations, either consciously or unconsciously. Researcher bias might be due to the use of Questionable Research Practices (QRPs). In research fields like medicine, blinding techniques have been applied to counteract researcher bias. In this paper, we present two studies to increase our body of knowledge on researcher bias in Software Engineering (SE) experiments, including: (i) QRPs potentially leading to researcher bias; (ii) causes behind researcher bias; and (iii) possible actions to counteract researcher bias with a focus on, but not limited to, blinding techniques. The former is an interview study, intended as an exploratory study, with nine experts of the empirical SE community. The latter is a quantitative survey with 51 respondents, who were experts of the above-mentioned community. The findings from the exploratory study represented the starting point to design the survey. In particular, we defined the questionnaire of this survey to support the findings from the exploratory study. From the interview study, it emerged that some QRPs (e.g., post-hoc outlier criteria) are acceptable in certain cases. Also, it appears that researcher bias is perceived in SE and, to counteract researcher bias, a number of solutions have been highlighted. For example, duplicating the data analysis in SE experiments or fostering open data policies in SE conferences/journals. The findings from the interview study are mostly confirmed by those from the survey, and allowed us to delineate recommendations to counteract researcher bias in SE experiments. Some recommendations are intended for SE researchers, while others are purposeful for the boards of SE research venues.

Study objective: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door–to–off-stretcher wait times that are applicable to a wide variety of emergency departments. Methods: Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. Results: There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. Conclusion: Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.

Context: Test-driven development (TDD) is an agile software development approach that has been widely claimed to improve software quality. However, the extent to which TDD improves quality appears to be largely dependent upon the characteristics of the study in which it is evaluated (e.g., the research method, participant type, programming environment, etc.). The particularities of each study make the aggregation of results untenable. Objectives: The goal of this paper is to: increase the accuracy and generalizability of the results achieved in isolated experiments on TDD, provide joint conclusions on the performance of TDD across different industrial and academic settings, and assess the extent to which the characteristics of the experiments affect the quality-related performance of TDD. Method: We conduct a family of 12 experiments on TDD in academia and industry. We aggregate their results by means of meta-analysis. We perform exploratory analyses to identify variables impacting the quality-related performance of TDD. Results: TDD novices achieve a slightly higher code quality with iterative test-last development (i.e., ITL, the reverse approach of TDD) than with TDD. The task being developed largely determines quality. The programming environment, the order in which TDD and ITL are applied, or the learning effects from one development approach to another do not appear to affect quality. The quality-related performance of professionals using TDD drops more than for students. We hypothesize that this may be due to their being more resistant to change and potentially less motivated than students. Conclusion: Previous studies seem to provide conflicting results on TDD performance (i.e., positive vs. negative, respectively). We hypothesize that these conflicting results may be due to different study durations, experiment participants being unfamiliar with the TDD process, or case studies comparing the performance achieved by TDD vs. the control approach (e.g

The significant momentum and importance of Mining Software Repositories (MSR) in Software Engineering (SE) has fostered new opportunities and challenges for extensive empirical research. However, MSR researchers seem to struggle to characterize the empirical methods they use into the existing empirical SE body of knowledge. This is especially the case of MSR experiments. To provide evidence on the special characteristics of MSR experiments and their differences with experiments traditionally acknowledged in SE so far, we elicited the hallmarks that differentiate an experiment from other types of empirical studies and characterized the hallmarks and types of experiments in MSR. We analyzed MSR literature obtained from a small-scale systematic mapping study to assess the use of the term experiment in MSR. We found that 19% of the papers claiming to be an experiment are indeed not an experiment at all but also observational studies, so they use the term in a misleading way. From the remaining 81% of the papers, only one of them refers to a genuine controlled experiment while the others stand for experiments with limited control. MSR researchers tend to overlook such limitations, compromising the interpretation of the results of their studies. We provide recommendations and insights to support the improvement of MSR experiments.

Desiderata is a general term for stakeholder needs, desires or preferences. Recent experiments demonstrate that presenting desiderata as templated requirements specifications leads to less creative solutions. However, these experiments do not establish how the presentation of desiderata affects design creativity. This study, therefore, aims to explore the cognitive mechanisms by which presenting desiderata as templated requirements specifications reduces creativity during software design. Forty-two software designers, organized into 21 pairs, participated in a dialog-based protocol study. Their interactions were transcribed and the transcripts were analyzed in two ways: (1) using inductive process coding and (2) using an a-priori coding scheme focusing on fixation and critical thinking. Process coding shows that participants exhibited seven categories of behavior: making design moves, uncritically accepting, rejecting, grouping, questioning, assuming and considering quality criteria. Closed coding shows that participants tend to accept given requirements and priority levels while rejecting newer, more innovative design ideas. Overall, the results suggest that designers fixate on desiderata presented as templated requirements specifications, hindering critical thinking. More precisely, requirements fixation mediates the negative relationship between specification formality and creativity.

Context: Previous studies have shown that training data instance selection based on nearest neighborhood (NN) information can lead to better performance in cross project defect prediction (CPDP) by reducing heterogeneity in training datasets. However, neighborhood calculation is computationally expensive and approximate methods such as Locality Sensitive Hashing (LSH) can be as effective as exact methods. Aim: We aim at comparing instance selection methods for CPDP, namely LSH, NN-filter, and Genetic Instance Selection (GIS). Method: We conduct experiments with five base learners, optimizing their hyper parameters, on 13 datasets from PROMISE repository in order to compare the performance of LSH with benchmark instance selection methods NN-Filter and GIS. Results: The statistical tests show six distinct groups for F-measure performance. The top two group contains only LSH and GIS benchmarks whereas the bottom two groups contain only NN-Filter variants. LSH and GIS favor recall more than precision. In fact, for precision performance only three significantly distinct groups are detected by the tests where the top group is comprised of NN-Filter variants only. Recall wise, 16 different groups are identified where the top three groups contain only LSH methods, four of the next six are GIS only and the bottom five contain only NN-Filter. Finally, NN-Filter benchmarks never outperform the LSH counterparts with the same base learner, tuned or non-tuned. Further, they never even belong to the same rank group, meaning that LSH is always significantly better than NN-Filter with the same learner and settings. Conclusions: The increase in performance and the decrease in computational overhead and runtime make LSH a promising approach. However, the performance of LSH is based on high recall and in environments where precision is considered more important NN-Filter should be considered.

Ubiquitous technologies such as mobile software applications (mobile apps) have a tremendous influence on the evolution of the social, cultural, economic, and political facets of life in society. Mobile apps fulfil many practical purposes for users including entertainment, transportation, financial management, etc. Given the ubiquity of mobile apps in the lives of individuals and the consequent effect of these technologies on society, it is essential to consider the relationship between human values and the development and deployment of mobile apps. The many negative consequences of violating human values such as privacy, fairness or social justice by technology have been documented in recent times. If we can detect these violations in a timely manner, developers can look to better address them. To understand the violation of human values in a range of common mobile apps, we analysed 22,119 app reviews from Google Play Store using natural language processing techniques. We base our values violation detection approach on a widely accepted model of human values; the Schwartz theory of basic human values. The results of our analysis show that 26.5% of the reviews contained text indicating user perceived violations of human values. We found that benevolence and self-direction were the most violated value categories, and conformity and tradition were the least violated categories. Our results also highlight the need for a proactive approach to the alignment of values amongst stakeholders and the use of app reviews as a valuable additional source for mining values requirements.

We are in the shoes of a practitioner who uses previous project releases’ data to predict which classes of the current release are defect-prone. In this scenario, the practitioner would like to use the most accurate classifier among the many available ones. A validation technique, hereinafter “technique”, defines how to measure the prediction accuracy of a classifier. Several previous research efforts analyzed several techniques. However, no previous study compared validation techniques in the within-project across-release class-level context or considered techniques that preserve the order of data. In this paper, we investigate which technique recommends the most accurate classifier. We use the last release of a project as the ground truth to evaluate the classifier’s accuracy and hence the ability of a technique to recommend an accurate classifier. We consider nine classifiers, two industry and 13 open projects, and three validation techniques: namely 10-fold cross-validation (i.e., the most used technique), bootstrap (i.e., the recommended technique), and walk-forward (i.e., a technique preserving the order of data). Our results show that: 1) classifiers differ in accuracy in all datasets regardless of their entity per value, 2) walk-forward outperforms both 10-fold cross-validation and bootstrap statistically in all three accuracy metrics: AUC of the selected classifier, bias and absolute bias, 3) surprisingly, all techniques resulted to be more prone to overestimate than to underestimate the performances of classifiers, and 3) the defect rate resulted in changing between the second and first half in both industry projects and 83% of open-source datasets. This study recommends the use of techniques that preserve the order of data such as walk-forward over 10-fold cross-validation and bootstrap in the within-project across-release class-level context given the above empirical results and that walk-forward is by nature more simple, inexpensive, and stable

Context: As a novel coronavirus swept the world in early 2020, thousands of software developers began working from home. Many did so on short notice, under difficult and stressful conditions. Objective: This study investigates the effects of the pandemic on developers’ wellbeing and productivity. Method: A questionnaire survey was created mainly from existing, validated scales and translated into 12 languages. The data was analyzed using non-parametric inferential statistics and structural equation modeling. Results: The questionnaire received 2225 usable responses from 53 countries. Factor analysis supported the validity of the scales and the structural model achieved a good fit (CFI = 0.961, RMSEA = 0.051, SRMR = 0.067). Confirmatory results include: (1) the pandemic has had a negative effect on developers’ wellbeing and productivity; (2) productivity and wellbeing are closely related; (3) disaster preparedness, fear related to the pandemic and home office ergonomics all affect wellbeing or productivity. Exploratory analysis suggests that: (1) women, parents and people with disabilities may be disproportionately affected; (2) different people need different kinds of support. Conclusions: To improve employee productivity, software companies should focus on maximizing employee wellbeing and improving the ergonomics of employees’ home offices. Women, parents and disabled persons may require extra support.

In the software industry, test automation is an investment, and its value to software development is, typically, only visible after a delay. The effects of adopting test automation approaches may be positive on both software quality and total costs through efficient testing and re-prioritizing work, as long as the right tools are in place. However, finding, evaluating, and selecting the right tool(s) for the purpose or context is problematic and may be essential to the success of a business. Furthermore, there is minimal empirical evidence available for evaluating experiential knowledge about these tools and processes for selecting them. The goal of this dissertation was to review and classify the state-of-the-practice of tool evaluation and selection (for software test automation) among software practitioners and contribute empirically validated evidence to the process. A mixed-methods approach was applied to expand the evidence base for the “voice of practitioners.” Both quantitative and qualitative data were collected via empirical methods, including quantitative surveys, web scraping, and data mining from Internet sources, as well as a grey literature review (GLR) and qualitative interviews. The key finding of this dissertation is that practitioners have reached an apparent consensus on the important criteria for tool evaluation. However, aligned guidelines and systematic processes for evaluating and selecting the right tool(s) are lacking. New tools for software testing are emerging in the market, and the findings reveal dominant tools, but local and global preferences may differ. The findings highlight the interconnection of different tool evaluation criteria and the context’s potential impact on them. The key lesson learned from the research suggests that empirical evidence via data triangulation is valuable in identifying false claims, justifying facts about the criteria, and revealing possible problems and misconceptions in the process of evaluatin, Tiivistelmä Ohjelmistoteollisuudessa testausautomaatio on investointi, jonka todelliset vaikutukset ohjelmistokehitykseen näkyvät viiveellä. Testausautomaatio voi vaikuttaa positiivisesti sekä ohjelmiston laatuun että kustannuksiin tehokkaan testauksen ja töiden uudelleenprioriosoinnin avulla, kunhan käytettävät työkalut ovat sopivia. Oikean työkalun/työkalujen löytäminen, arviointi ja valinta tiettyyn tarkoitukseen tietyssä kontekstissa on ongelmallista ja voi olla olennaista liiketoiminnan menestymiselle. Työkalujen arviointiin ja valintaan liittyvästä kokemusperäisestä tiedosta on vain vähän empiiristä näyttöä. Väitöskirjan tavoitteena oli tarkastella ja luokitella ohjelmistoteollisuuden ammattilaisten keskuudessa vallitsevia käytäntöjä ohjelmistotestauksen työkalujen arviointiin ja valintaan, sekä tuottaa prosessiin empiiristä tutkimusnäyttöä. Monimenetelmätutkimusta hyödynnettiin laajentamaan tutkimusnäyttöä ”ammattilaisten äänestä”. Useita empiirisiä menetelmiä käytettiin keräämään sekä määrällistä että laadullista dataa: määrällisiä kyselyitä, verkkosivujen haravointia, tiedon louhintaa Internet-lähteistä, harmaan kirjallisuuden analyysiä sekä haastatteluita. Tämän väitöskirjan keskeinen löydös on, että ammattilaisilla on näennäinen yksimielisyys työkalun arvioinnin tärkeistä kriteereistä, mutta oikean työkalun arvioimiseen ja valintaan ei ole yhdenmukaisia ohjeita eikä systemaattisia prosesseja. Samalla, kun uusia ohjelmistotestauksen työkaluja ilmaantuu markkinoille, tulokset paljastavat hallitsevia työkaluja, vaikka preferensseissä on globaaleja ja paikallisia eroja. Havainnot korostavat eri arviointikriteerien yhteyttä ja kontekstin mahdollista vaikutusta kriteereihin. Tutkimuksen keskeinen tulos esittää, että empiirinen tutkimusnäyttö on arvokasta, koska sen avulla voidaan tuoda ilmi virheellisiä väitteitä ja osoittaa tosiasioita kriteerien suhteen, sekä paljastaa ongelmia ja väärinkäsityksiä työkalujen arviointi- ja valintaprosesseissa. Akateeminen tutk

Software engineering (SE) experiments suffer from threats to validity that may impact their results. Replication allows researchers building on top of previous experiments’ weaknesses and increasing the reliability of the findings. Illustrating the benefits of replication to increase the reliability of the findings and uncover moderator variables. We replicate an experiment on test-driven development (TDD) and address some of its threats to validity and those of a previous replication. We compare the replications’ results and hypothesize on plausible moderators impacting results. Differences across TDD replications’ results might be due to the operationalization of the response variables, the allocation of subjects to treatments, the allowance to work outside the laboratory, the provision of stubs, or the task. Replications allow examining the robustness of the findings, hypothesizing on plausible moderators influencing results, and strengthening the evidence obtained.

Design creativity, the originality and practicality of a solution concept is critical for the success of many software projects. However, little research has investigated the relationship between the way desiderata are presented and design creativity. This study therefore investigates the impact of presenting desiderata as ideas, requirements or prioritized requirements on design creativity. Two between-subjects randomized controlled experiments were conducted with 42 and 34 participants. Participants were asked to create design concepts from a list of desiderata. Participants who received desiderata framed as requirements or prioritized requirements created designs that are, on average, less original but more practical than the designs created by participants who received desiderata framed as ideas. This suggests that more formal, structured presentations of desiderata are less appropriate where a creative solution is desired. The results also show that design performance is highly susceptible to minor changes in the vernacular used to communicate desiderata.

Background: Test-Driven Development (TDD) is an iterative software development process characterized by test-code-refactor cycle. TDD recommends that developers work on small and manageable tasks at each iteration. However, the ability to break tasks into small work items effectively is a learned skill that improves with experience. In experimental studies of TDD, the granularity of task descriptions is an overlooked factor. In particular, providing a more granular task description in terms of a set of sub-tasks versus providing a coarser-grained, generic description. Objective: We aim to investigate the impact of task description granularity on the outcome of TDD, as implemented by novice developers, with respect to software quality, as measured by functional correctness and functional completeness. Method: We conducted a one-factor crossover experiment with 48 graduate students in an academic environment. Each participant applied TDD and implemented two tasks, where one of the tasks was presented using a more granular task description. Resulting artifacts were evaluated with acceptance tests to assess functional correctness and functional completeness. Linear mixed-effects models (LMM) were used for analysis. Results: Software quality improved significantly when participants applied TDD using more granular task descriptions. The effect of task description granularity is statistically significant and had a medium to large effect size. Moreover, the task was found to be a significant predictor of software quality which is an interesting result (because two tasks used in the experiment were considered to be of similar complexity). Conclusion: For novice TDD practitioners, the outcome of TDD is highly coupled with the ability to break down the task into smaller parts. For researchers, task selection and task description granularity requires more attention in the design of TDD experiments. Task description granularity should be taken into account in secondary

There is a broad consensus in the software engineering (SE) research community that understanding system desiderata and design creativity is critical for the success of software projects. This has motivated a plethora of research in SE to improve requirements engineering (RE) processes. However, little research has investigated the relationship between the way desiderata are presented (i.e., framed) and creative design performance. This dissertation, therefore, examines the effects of more formal presentations of desiderata on design creativity. The research was conducted in three phases. The first consisted of summarizing the available literature on cognitive biases in SE to build a comprehensive body of knowledge, understand the current state of research, and identify the relevant literature to position and delineate subsequent investigations involving the framing effect and fixation. This research phase also investigated how creativity is conceptualized (i.e., understood, assessed and improved) in SE by exploring the perceptual differences and similarities between SE researchers and practitioners. In the second phase, two controlled experiments were conducted to investigate the impact of framing desiderata first as requirements (in general) and then as prioritized requirements on design creativity (i.e., the originality and practicality of a design). The third phase involved a protocol study to explore the underlying cognitive mechanisms that may explain why framing desiderata as formal requirements affects creativity. The empirical evidence from the second and third phases was interpreted together to propose a theoretical framework that explains the effect of specification formality on design creativity. While the results of the experiments show that specification formality is negatively related to design creativity (i.e., desiderata framed as requirements or prioritized requirements result in designs that are less creative), the findings from the proto, Tiivistelmä Ohjelmistotuotannon tutkijoiden keskuudessa on laaja yksimielisyys järjestelmän tarpeiden ja suunnittelun luovuuden ymmärtämisen kriittisyydestä ohjelmistoprojektien menestyksessä. Tämä on motivoinut monia ohjelmistotuotannon vaatimusmäärittelyprosessien parantamiseen liittyviä tutkimuksia. Harvassa on tarkasteltu tarpeiden esitystavan (eli muotoilun) ja luovan suunnittelun lopputuloksen välistä yhteyttä. Tässä väitöskirjassa tarkastellaan tarpeiden muodollisempien esitystapojen vaikutuksia suunnittelun luovuuteen. Tutkimus oli kolmivaiheinen. Ensin referoitiin ohjelmistotuotannossa kognitiivisiin harhoihin liittyvä kirjallisuus kartoittamaan nykytutkimuksen tila ja merkityksellinen kirjallisuus myöhempien, kehysvaikutuksen ja fiksaation sisältävien tutkimusten sijoittamiseen ja rajaamiseen. Lisäksi tarkasteltiin luovuuden käsitteellistämistä (eli ymmärrettävyyttä, arviointia ja parantamista) tutkimalla katsannollisia eroja ja yhtäläisyyksiä tutkijoiden ja ammattilaisten välillä. Toisessa vaiheessa tehtiin kaksi kontrolloitua koetta tarpeiden muotoilun vaikutuksien tutkimiseksi, ensin vaatimuksina (yleisesti) ja sitten tärkeysjärjestykseen laitettuina vaatimuksina suhteessa suunnittelun luovuuteen (eli omaperäisyyteen ja käytännöllisyyteen). Lopuksi, protokollatutkimuksella selvitettiin taustalla olevia kognitiivisia mekanismeja selittämään syitä muodollisina vaatimuksina esitettyjen tarpeiden vaikutuksista luovuuteen. Toisesta ja kolmannesta vaiheesta saatujen empiiristen aineistojen tulkittiin yhdessä muodostavan teoreettisen viitekehyksen, joka selittää määrittelyn muodollisuuden vaikutusta suunnittelun luovuuteen. Vaikka kokeiden tulokset osoittavat määrittelyjen muodollisuuden vaikuttavan negatiivisesti suunnittelun luovuuteen (eli tarpeiden muotoilu vaatimuksina tai priorisoituina vaatimuksina vähentää suunnitelmien luovuutta), protokollatutkimuksen tulokset viittaavat fiksaation vaikuttavan negatiiviseen yhteyteen määrittelyjen muodollisuuden ja s

Background: Confirmation bias is the tendency to search for evidence that confirms a person’s preconceptions. Confirmation bias among software testers is their tendency to validate the correct functioning of the program rather than testing it to reveal errors. Psychology literature suggests that time pressure may promote confirmation bias because time pressure impedes analytical processing of the task at hand. Time pressure is perceived negatively for its effects in software engineering (SE), therefore, its effect on confirmation bias may exacerbate software quality. Objective: We aim to examine confirmation bias among software testers. Additionally, we examine the effect of time pressure on confirmation bias and how time pressure affects the testers’ perception of the performance. We also question what other antecedents to confirmation bias exist in software testing and how they lead to it. Method: We first examined the state of the art research on cognitive biases in SE using systematic mapping. Then, we empirically examined the feasibility of using students in further experiments. An experiment with 42 students (novice professionals) investigated the manifestation of confirmation bias and whether time pressure promotes it. Another experiment with 87 novice professionals examined the perception of the performance of software testers under time pressure. A grounded theory study based on the interview-data of 12 practitioners explored other antecedents to confirmation bias in software testing and how they lead to it. Results: Time pressure emerged as a major antecedent to confirmation bias in the grounded theory. Testers prefer to validate the correct functioning of the program under time pressure. However, time pressure could not significantly promote confirmation bias among testers. Software testers significantly manifest confirmation bias irrespective of time pressure. The perception of performance is also sustained irrespective of time pressure. Conclus, Tiivistelmä Tausta: Vahvistusharha tarkoittaa taipumusta hakea ennakko-odotuksia vahvistavaa todistusaineistoa. Ohjelmistotestaajien vahvistusharha tarkoittaa taipumusta varmistaa ohjelmiston oikea toiminta mieluummin kuin hakea siitä virheitä. Psykologinen tutkimus esittää, että aikataulupaine voi lisätä vahvistusharhaa heikentämällä työn analyyttista tarkastelua. Aikataulupainetta pidetään ohjelmistotekniikan soveltamiseen kielteisesti vaikuttavana asiana, minkä johdosta sen vaikutus vahvistusharhaan voi heikentää ohjelmiston laatua. Tavoite: Tarkastelimme ohjelmistotestaajien vahvistusharhaa tutkimalla aikataulupaineen vaikutusta vahvistusharhaan ja testaajien käsitykseen testauksen tehokkuudesta. Lisäksi kysymme, mitkä muut tekijät johtavat ohjelmistotestauksen vahvistusharhaan, ja millä tavoin. Menetelmä: Ensiksi tarkastelimme ohjelmistotekniikan kognitiivisten harhojen viimeisintä tutkimusta systemaattista kirjallisuuskartoituksella. Sitten tutkimme kokeellisesti, miten yliopisto-opiskelijat soveltuvat käytettäväksi tutkimusjoukkona vahvistusharhan kokeellisessa tutkimuksessa. Kokeellinen tutkimus, johon osallistui 42 opiskelijaa (aloittelevaa ammattilaista), tarkasteli vahvistusharhan lisääntymistä aikataulupaineen vaikutuksesta. Toinen kokeellinen tutkimus, johon osallistui 87 aloittelevaa ammattilaista, tarkasteli ohjelmistotestaajien käsitystä testauksen tehokkuudesta aikataulupaineen alla. Kahdeltatoista ammattilaiselta haastattelemalla kerätystä tutkimusaineistosta tarkasteltiin ankkuroidun teorian menetelmällä muiden mahdollisten tekijöiden vaikutusta vahvistusharhaan. Tulokset: Ankkuroidussa teoriassa aikataulupaine osoittautui merkittäväksi vahvistusharhan tekijäksi. Ammattimaiset ohjelmistotestaajat haluavat mieluummin validoida ohjelmiston oikean toiminnan aikataulupaineessa. Toisessa kokeellisessa tutkimuksessa aikataulupaine ei kuitenkaan lisännyt merkittävästi testaajien vahvistusharhaa, vaan testaajien vahvistusharha ilmeni merkittävästi aikatau

Context: This study contributes to the understanding of the current state of cross-project defect prediction (CPDP) by investigating the topic in themes, with special focus on data approaches and covering search-based training data selection, by proposing data selection methods and investigating their impact. The empirical evidence for this work is collected through a formal systematic literature review method for the review, and from experiments on open source projects. Objective: We aim to understand and summarize the manner in which various data manipulation approaches are used in CPDP and their potential impacts on performance. Further, we aim at utilizing search-based methods to produce evolving training data sets to filter irrelevant instances from other projects before training. Method: Through a series of studies following the literature review of current state of CPDP, we propose a search-based method called genetic instance selection (GIS). We validate our initial findings by conducting the next study on a large set of data sets with multiple feature sets. We refine our design decisions using an exploratory study. Finally, we investigate an existing meta-learning approach, provide insights on its design and propose an alternative iterative data selection method. Results: The literature review reveals lower performances of CPDP in comparison with within project defect prediction (WPDP) models and provides a set of primary studies to be used as the basis for future research. Our proposed data selection methods make the case for search-based approaches considering their higher effectiveness and performance. We identified potential impacting factors on the effectiveness through the exploratory study and proposed methods to create better CPDP models. Conclusions: The proposal of numerous approaches in the literature over the last decade has led to progress in the area and the acquired knowledge and tools apply to many similar domains and can act as par, Tiivistelmä Tausta: Tämä tutkimus edistää projektienvälisten virheiden ennustamisen nykytilan ymmärtämistä (CPDP) tutkimalla aihetta teemoissa, keskittyen erityisesti tiedollisiin lähestymistapoihin ja hakuperusteisen harjoitusdatan valintaan esittelemällä datan valintamenetelmiä ja tutkimalla niiden vaikutuksia. Tämän työn empiirinen todistusaineisto on koottu muodollisella systemaattisella kirjallisuuskatsauksella ja avoimen lähdekoodin projekteissa tehdyillä kokeilla. Tavoite: Pyrimme ymmärtämään ja tiivistämään tavan, jolla erilaisia datan käsittelyn lähestymistapoja käytetään CPDP:ssa sekä niiden potentiaalisia vaikutuksia suorituskykyyn. Lisäksi, tavoitteenamme on hyödyntää hakuperusteisia menetelmiä muodostamaan kehittyviä harjoitusdata-settejä suodattamaan epäolennaisia esiintymiä muista projekteista ennen koulutusta. Menetelmä: CPDP:n nykytilan kirjallisuuskatsauksen jälkeen tehtyjen tutkimusten avulla ehdotamme hakuperusteista menetelmää, jota kutsutaan geneettisen esiintymän valinnaksi (GIS). Todistamme alustavat havaintomme suorittamalla seuraavan tutkimuksen suurella joukolla datasettejä, joilla on useita ominaisuuksia. Jalostamme suunnittelupäätöksiämme käyttäen tutkivaa tutkimusta. Lopuksi, tutkimme vallitsevaa meta-oppimisen lähestymistapaa ja tarjoamme näkemyksiä sen suunnitteluun ja ehdotamme vaihtoehtoista, toistuvaa datan valintamenetelmää. Tulokset: Kirjallisuuskatsaus paljastaa CPDP:n heikomman suorituskyvyn verrattuna projektinsisäisten virheiden ennustamisen (WPDP) malleihin ja tarjoaa joukon primaaritutkimuksia, joita voidaan käyttää perustana myöhemmälle tutkimukselle. Ehdottamamme datan valintamenetelmät puoltavat hakuperusteisten menetelmiä niiden paremman tehokkuuden ja suorituskyvyn vuoksi. Tunnistimme potentiaalisia tehokuuteen vaikuttavia tekijöitä tutkivien tutkimusten avulla ja ehdotimme metodeja parempien CPDP mallien luomiseksi. Johtopäätökset: Viime vuosikymmenten aikana kirjallisuudessa esitellyt lukuisat menetelmät ovat edistän

Background: Cross project defect prediction (CPDP) recently gained considerable attention, yet there are no systematic efforts to analyse existing empirical evidence. Objective: To synthesise literature to understand the state-of-the-art in CPDP with respect to metrics, models, data approaches, datasets and associated performances. Further, we aim to assess the performance of CPDP versus within project DP models. Method: We conducted a systematic literature review. Results from primary studies are synthesised (thematic, meta-analysis) to answer research questions. Results: We identified 30 primary studies passing quality assessment. Performance measures, except precision, vary with the choice of metrics. Recall, precision, f-measure, and AUC are the most common measures. Models based on Nearest-Neighbour and Decision Tree tend to perform well in CPDP, whereas the popular naïve Bayes yields average performance. Performance of ensembles varies greatly across f-measure and AUC. Data approaches address CPDP challenges using row/column processing, which improve CPDP in terms of recall at the cost of precision. This is observed in multiple occasions including the meta-analysis of CPDP versus WPDP. NASA and Jureczko datasets seem to favour CPDP over WPDP more frequently. Conclusion: CPDP is still a challenge and requires more research before trustworthy applications can take place. We provide guidelines for further research.

Numerous software companies are adopting value-based decision making. However, what does value mean for key stakeholders making decisions? How do different stakeholder groups understand value? Without an explicit understanding of what value means, decisions are subject to ambiguity and vagueness, which are likely to bias them. This case study provides an in-depth analysis of key stakeholders’ value propositions when selecting features for a large telecommunications company’s software-intensive product. Stakeholder’ value propositions were elicited via interviews, which were analyzed using Grounded Theory coding techniques (open and selective coding). Thirty-six value propositions were identified and classified into six dimensions: customer value, market competitiveness, economic value/profitability, cost efficiency, technology & architecture, and company strategy. Our results show that although propositions in the customer value dimension were those mentioned the most, the concept of value for feature selection encompasses a wide range of value propositions. Moreover, stakeholder groups focused on different and complementary value dimensions, calling to the importance of involving all key stakeholders in the decision making process. Although our results are particularly relevant to companies similar to the one described herein, they aim to generate a learning process on value-based feature selection for practitioners and researchers in general.

Context: Previous studies have shown that steered training data or dataset selection can lead to better performance for cross project defect prediction(CPDP). On the other hand, feature selection and data quality are issues to consider in CPDP. Objective: We aim at utilizing the Nearest Neighbor (NN)-Filter, embedded in genetic algorithm to produce validation sets for generating evolving training datasets to tackle CPDP while accounting for potential noise in defect labels. We also investigate the impact of using different feature sets. Method: We extend our proposed approach, Genetic Instance Selection (GIS), by incorporating feature selection in its setting. We use 41 releases of 11 multi-version projects to assess the performance GIS in comparison with benchmark CPDP (NN-filter and Naive-CPDP) and within project (Cross-Validation(CV) and Previous Releases(PR)). To assess the impact of feature sets, we use two sets of features, SCM+OO+LOC(all) and CK+LOC(ckloc) as well as iterative info-gain subsetting(IG) for feature selection. Results: GIS variant with info gain feature selection is significantly better than NN-Filter (all,ckloc,IG) in terms of F1 (\(\mathit{p} = \mathit{values} ≪ 0.001,\) Cohen’s \(\mathit{d} = \{0.621, 0.845, 0.762\}\)) and G (\(\mathit{p} = \mathit{values} ≪ 0.001,\) Cohen’s \(\mathit{d} = \{0.899, 1.114, 1.056\}\)), and Naive CPDP (all,ckloc,IG) in terms of F1 (\(\mathit{p} = \mathit{values} ≪ 0.001,\) Cohen’s \(\mathit{d} = \{0.743, 0.865, 0.789\}\)) and G (\(\mathit{p} = \mathit{values} ≪ 0.001,\) Cohen’s \(\mathit{d} = \{1.027, 1.119, 1.050\}\)). Overall, the performance of GIS is comparable to that of within project defect prediction (WPDP) benchmarks, i.e. CV and PR. In terms of multiple comparisons test, all variants of GIS belong to the top ranking group of approaches. Conclusions: We conclude that datasets obtained from search based approaches combined with feature selection techniques is a promis

Context: Controlled experiments are an important empirical method to generate and validate theories. Many software engineering experiments are conducted with students. It is often claimed that the use of students as participants in experiments comes at the cost of low external validity while using professionals does not. Objective We believe a deeper understanding is needed on the external validity of software engineering experiments conducted with students or with professionals. We aim to gain insight about the pros and cons of using students and professionals in experiments. Method We performed an unconventional, focus group approach and a follow-up survey. First, during a session at ISERN 2014, 65 empirical researchers, including the seven authors, argued and discussed the use of students in experiments with an open mind. Afterwards, we revisited the topic and elicited experts’ opinions to foster discussions. Then we derived 14 statements and asked the ISERN attendees excluding the authors, to provide their level of agreement with the statements. Finally, we analyzed the researchers’ opinions and used the findings to further discuss the statements. Results Our survey results showed that, in general, the respondents disagreed with us about the drawbacks of professionals. We, on the contrary, strongly believe that no population (students, professionals, or others) can be deemed better than another in absolute terms. Conclusion Using students as participants remains a valid simplification of reality needed in laboratory contexts. It is an effective way to advance software engineering theories and technologies but, like any other aspect of study settings, should be carefully considered during the design, execution, interpretation, and reporting of an experiment. The key is to understand which developer population portion is being represented by the participants in an experiment. Thus, a proposal for describing experimental participants is put forward.

Context: Confirmation bias is a person’s tendency to look for evidence that strengthens his/her prior beliefs rather than refutes them. Manifestation of confirmation bias in software testing may have adverse effects on software quality. Psychology research suggests that time pressure could trigger confirmation bias. Objective: In the software industry, this phenomenon may deteriorate software quality. In this study, we investigate whether testers manifest confirmation bias and how it is affected by time pressure in functional software testing. Method: We performed a controlled experiment with 42 graduate students to assess manifestation of confirmation bias in terms of the conformity of their designed test cases to the provided requirements specification. We employed a one factor with two treatments between-subjects experimental design. Results: We observed, overall, participants designed significantly more confirmatory test cases as compared to disconfirmatory ones, which is in line with previous research. However, we did not observe time pressure as an antecedent to an increased rate of confirmatory testing behaviour. Conclusion: People tend to design confirmatory test cases regardless of time pressure. For practice, we find it necessary that testers develop self-awareness of confirmation bias and counter its potential adverse effects with a disconfirmatory attitude. We recommend further replications to investigate the effect of time pressure as a potential contributor to the manifestation of confirmation bias.

Background: Time-pressure is an inevitable reality of software industry that influences the performance of software engineers. It may result in adverse effects on software quality or distort the perception of performance on executed tasks to differ from actual performance. Objective: We aim to investigate the effect of time-pressure on perceived and actual performance of software testers in the context of functional software testing. Method: We performed two controlled experiments with 87 graduate students in two academic terms. We assessed actual performance in terms of coverage (i.e. percentage of test cases correctly identified) and perceived performance using NASA-TLX. We have an independent factorial design for our experimental study. Results: The results reveal a significant effect of time-pressure on actual performance. However, we could not observe a significant effect of time-pressure on the perceived performance of the participants for the task undertaken. We also observed a significant negative correlation between actual and perceived performance when controlled for time-pressure and experimental session factors. Conclusions: Time-pressure affects the actual performance in a testing task but the perception of accomplishment by the testers is sustained irrespective of time-pressure, indicating an over-estimation issue. Perception of performance should be adjusted to align with reality to account for the effect of time pressure. This will lead to better self estimates of performance.

Context: Search based approaches are gaining attention in cross project defect prediction (CPDP). The complexity of such approaches and existence of various design decisions are important issues to consider. Objective: We aim at investigating factors that can affect the performance of search based selection (SBS) approaches. We study a genetic instance selection approach (GIS) and present an evaluation of design options for search based CPDP. Method: Using an exploratory approach, data from different options of models are gathered and analyzed through ANOVA tests and effect sizes. Results: Both feature sets and validation dataset selection options show small or insignificant impacts on F-measure and precision, unlike the more affected false positive and true negative rates. Size of training data does not seem to be related to significant changes in F-measure and precision and high variability in performance are discouraging evidence for using larger datasets. Fitness function is one of the major factors that impact performance with much larger effect than the choice of validation dataset. Finally, while showing slight impacts, data label changes do not seem to be the top contributor to performance. Conclusions: We conclude that exploratory approaches can be effective for making design decisions in constructing search based CPDP models. Effect of individual tuned learners and their interaction with other affecting parameters and more in depth study of quality affecting factors guided by label changes are directions to investigate.