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Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models. [ABSTRACT FROM AUTHOR]

Researchers are increasingly recognizing the importance of human aspects in software development. Because qualitative methods are used to explore human behavior in‐depth, we believe that studies using such methods will become more common. Existing qualitative software engineering guidelines do not cover the full breadth of qualitative methods and the knowledge on how to use them like in social sciences. The purpose of this study was to extend the software engineering community's current body of knowledge regarding available qualitative methods and their quality assurance frameworks and to provide recommendations and guidelines for their use. With the support of an epistemological argument and a survey of the literature, we suggest that future research would benefit from (1) utilizing a broader set of research methods, (2) more strongly emphasizing reflexivity, and (3) employing qualitative guidelines and quality criteria. We present an overview of three qualitative methods commonly used in social sciences but rarely seen in software engineering research, namely interpretative phenomenological analysis, narrative analysis, and discourse analysis. Furthermore, we discuss the meaning of reflexivity in relation to the software engineering context and suggest means of fostering it. Our paper will help software engineering researchers better select and then guide the application of a broader set of qualitative research methods. [ABSTRACT FROM AUTHOR]

Summary: Software stakeholders report bugs in issue tracking system (ITS) with manually labeled priorities. However, the lack of knowledge and standard for prioritization may cause stakeholders to mislabel the priorities. In response, priority predictors are actively developed to support them. Prior studies trained machine learners based on textual similarity, categorical, and numeric technical features of bug reports. Most models were validated by time‐insensitive approaches, and they were producing suboptimal results for practical usage. While they ignored the social aspects of ITS, the technical aspects were also limited in surface features of bug reports. To better model the bug report, we extract their topic and most similar code structures. Since ITS bridges users and developers as the main contributors, we also integrate their experience, sentiment, and socio‐technical features to construct a new dataset. Then, we perform two‐classed and multiclassed bug priority prediction based on the dataset. We also introduce adversarial training using generated training data with random word swap and random word deletion. We validate our model in within‐project, cross‐project, and time‐wise scenarios, and it outperforms the two baselines by up to 15% in area under curve‐receiver operating characteristics (AUC‐ROC) and 19% in Matthews correlation coefficient (MCC). We reveal involving contributor (i.e., assignee and reporter) features such as sentiment that could boost prediction performance. Finally, we test statistically the mean and distribution of the features that reflect the differences in social and technical aspects (e.g., quality of communication and resource distribution) between high and low priority reports. In conclusion, we suggest that researchers should consider both social and technical aspects of ITS in bug report priority prediction and introduce adversarial training to boost model performance. [ABSTRACT FROM AUTHOR]

Reflecting on the limitations of a study is a crucial part of the research process. In software engineering studies, this reflection is typically conveyed through discussions of study limitations or threats to validity. In current practice, such discussions seldom provide sufficient insight to understand the rationale for decisions taken before and during the study, and their implications. We revisit the practice of discussing study limitations and threats to validity and identify its weaknesses. We propose to refocus this practice of self-reflection to a discussion centered on the notion of trade-offs. We argue that documenting trade-offs allows researchers to clarify how the benefits of their study design decisions outweigh the costs of possible alternatives. We present guidelines for reporting trade-offs in a way that promotes a fair and dispassionate assessment of researchers' work. [ABSTRACT FROM AUTHOR]

Generative AI is only a few years old but already being applied in Software Engineering (SE). This literature review examines the most popular SE sub-fields of such cases and research methods that are typically used. 117 studies starting from 2020 have been assessed, and literature review has shown that the most active research is ongoing in the code generation area. It is not clearly defined by researchers, but the majority of the methods can be assumed as experiments. It is concluded that researchers often do not define the used research method with exclusions such as literature review or opinion survey. However, different validation methods are highly valued and applied thoroughly. [ABSTRACT FROM AUTHOR]

Requirements collection is difficult and a critical phase of software development life cycle, in particular in a global software development (GSD) environment. In GSD, clients and vendors are physically separated such that there exist challenges such as lack of face‐to‐face communication, language differences, culture variation, and time zone differences. The objective of current research is to identify critical challenges of requirement engineering in GSD. There is no work done yet to empirically analyze all possible challenges via questionnaire survey from software industries. This research paper empirically investigates and analyze the identified challenges from systematic literature review (SLR) based on a questionnaire survey. For this purpose, 50 respondents from different countries are organized. From this research, 13 challenges during requirements implementation in the context of GSD have been identified previously via SLR. These challenges were then evaluated using an empirical approach of questionnaire survey. The results from respondents were analyzed based on type of respondents, level of experience of respondents, and from client–vendor perspective. Finally, the challenges were prioritized based on its frequency of occurrences from the SLR and the questionnaire survey. The relationships between the challenges and the survey results were evaluated using the Spearman's correlation coefficient. The results produced a 0.835 Spearman's correlation coefficient at significance level ρ = 0.000, which showed a strong positive correlation between the outcome of SLR and survey with no significant difference. The implication of this research work lies in both fundamental and practical perspective. The prioritized set of challenges was provided based on SLR, and questionnaire survey acts as a knowledge base for both researchers and industrial practitioners. This work will help researchers to identify challenges in GSD projects or other software engineering areas. [ABSTRACT FROM AUTHOR]

Software engineering (SE) requires developers to collaborate with stakeholders, and understanding their emotions and perspectives is often vital. Empathy is a concept characterising a person's ability to understand and share the feelings of another. However, empathy continues to be an under-researched human aspect in SE. We studied how empathy is practised between developers and end users using a mixed methods case study. We used an empathy test, observations, and interviews to collect data and socio-technical grounded theory and descriptive statistics to analyse data. We identified the nature of awareness required to trigger empathy and enablers of empathy. We discovered barriers to empathy and a set of potential strategies to overcome these barriers. We report insights on emerging relationships and present a set of recommendations and potential future works on empathy and SE for software practitioners and SE researchers. [ABSTRACT FROM AUTHOR]

Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents to software engineering researchers the six key challenges that a domain expert faces in addressing their problem with a computational workflow, and the underlying executable implementation. These challenges arise out of our conceptual framework which presents the "route" of transformations that a domain expert may choose to take while developing their solution. To ground our conceptual framework in the state of the practice, this article discusses a selection of available textual and graphical workflow systems and their support for the transformations described in our framework. Example studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain specificity and machine learning usage of their problem, workflow, and implementation. The state of the practice informs our discussion of the six key challenges, where we identify which challenges and transformations are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains. [ABSTRACT FROM AUTHOR]

Enterprise-resource-planning (ERP) systems are highly complex, incorporating critical data and configuration options that can easily cause security threats or risks. While product-line engineering (PLE) provides methods for dealing with configurability, security concerns are not of primary concern in established PLE frameworks. In this paper, we extend the perspectives of established PLE frameworks to incorporate security engineering for ERP systems. We build on a literature review of 15 years (2008-2022) of research on security engineering for ERP systems or product lines. Our framework incorporates three perspectives (i.e., application, security, and domain engineering) with 16 processes and 24 activities. We discuss our framework with respect to the ERP reference architecture and relevant security concerns. Our contributions are intended to help researchers and practitioners obtain a reference on how security engineering can be integrated into PLE for ERP systems. [ABSTRACT FROM AUTHOR]

Technical debt (TD) is a metaphor used to refer to the added software system costs acquired from taking shortcuts. Unfortunately, large amounts of TD can lead to serious consequences, and, thus, the management of TD is essential. Due to TD being a relatively new subject of study, many aspects of TD remain ambiguous. Fortunately, Twitter has been proven to hold a wealth of information on many subjects. As such, this survey study aims to gain a better understanding on how interest in TD has evolved over time and how TD is addressed on Twitter. A total of 128,897 TD‐related tweets were scrapped from Twitter and analyzed using a number of proxy measures and Latent Dirichlet Allocation (LDA). The results revealed that interest in TD on Twitter has been generally increasing since the platform's early stages. Furthermore, TD‐related tweets were found to revolve around 11 distinct categories. The TD in games category was discovered to be the most popular category, followed by TD communication and TD repayment. The results highlight that TD is a diverse and overarching topic that contains many potential avenues for further exploration. Software engineering researchers, practitioners, and educators can utilize this study to help steer their TD‐related future efforts. [ABSTRACT FROM AUTHOR]

The tremendous success of Stack Overflow has accumulated an extensive corpus of software engineering knowledge, thus motivating researchers to propose various solutions for analyzing its content. The performance of such solutions hinges significantly on the selection of representation models for Stack Overflow posts. As the volume of literature on Stack Overflow continues to burgeon, it highlights the need for a powerful Stack Overflow post representation model and drives researchers' interest in developing specialized representation models that can adeptly capture the intricacies of Stack Overflow posts. The state-of-the-art (SOTA) Stack Overflow post representation models are Post2Vec and BERTOverflow, which are built upon neural networks such as convolutional neural network and transformer architecture (e.g., BERT). Despite their promising results, these representation methods have not been evaluated in the same experimental setting. To fill the research gap, we first empirically compare the performance of the representation models designed specifically for Stack Overflow posts (Post2Vec and BERTOverflow) in a wide range of related tasks (i.e., tag recommendation, relatedness prediction, and API recommendation). The results show that Post2Vec cannot further improve the SOTA techniques of the considered downstream tasks, and BERTOverflow shows surprisingly poor performance. To find more suitable representation models for the posts, we further explore a diverse set of transformer-based models, including (1) general domain language models (RoBERTa, Longformer, and GPT2) and (2) language models built with software engineering related textual artifacts (CodeBERT, GraphCodeBERT, seBERT, CodeT5, PLBart, and CodeGen). This exploration shows that models like CodeBERT and RoBERTa are suitable for representing Stack Overflow posts. However, it also illustrates the "No Silver Bullet" concept, as none of the models consistently wins against all the others. Inspired by the findings, we propose SOBERT, which employs a simple yet effective strategy to improve the representation models of Stack Overflow posts by continuing the pre-training phase with the textual artifact from Stack Overflow. The overall experimental results demonstrate that SOBERT can consistently outperform the considered models and increase the SOTA performance significantly for all the downstream tasks. [ABSTRACT FROM AUTHOR]

In the software engineering (SE) community, deep learning (DL) has recently been applied to many source code processing tasks, achieving state-of-the-art results. Due to the poor interpretability of DL models, their security vulnerabilities require scrutiny. Recently, researchers have identified an emergent security threat to DL models, namely, poison attacks. The attackers aim to inject insidious backdoors into DL models by poisoning the training data with poison samples. The backdoors mean that poisoned models work normally with clean inputs but produce targeted erroneous results with inputs embedded with specific triggers. By using triggers to activate backdoors, attackers can manipulate poisoned models in security-related scenarios (e.g., defect detection) and lead to severe consequences. To verify the vulnerability of deep source code processing models to poison attacks, we present a poison attack approach for source code named CodePoisoner as a strong imaginary enemy. CodePoisoner can produce compilable and functionality-preserving poison samples and effectively attack deep source code processing models by poisoning the training data with poison samples. To defend against poison attacks, we further propose an effective poison detection approach named CodeDetector. CodeDetector can automatically identify poison samples in the training data. We apply CodePoisoner and CodeDetector to six deep source code processing models, including defect detection, clone detection, and code repair models. The results show that ❶ CodePoisoner conducts successful poison attacks with a high attack success rate (average: 98.3%, maximum: 100%). It validates that existing deep source code processing models have a strong vulnerability to poison attacks. ❷ CodeDetector effectively defends against multiple poison attack approaches by detecting (maximum: 100%) poison samples in the training data. We hope this work can help SE researchers and practitioners notice poison attacks and inspire the design of more advanced defense techniques. [ABSTRACT FROM AUTHOR]

Aim/Purpose The objective of this study is to summarize the challenges of Agile Requirements Engineering (Agile RE) in the public sector in republican and constitutional monarchy nations. Additionally, it offers recommendations to address these challenges. Background Failure of IT projects in the public sector results in financial losses for the state and loss of public trust, often attributed to issues in requirements engineering such as prioritization of user needs and excessive scope of requirements. IT projects can have a higher success rate with Agile RE, but there are also drawbacks. Therefore, this study holds significance by presenting a thorough framework designed to pinpoint and overcome the challenges associated with Agile RE to increase the success rate of IT projects. Methodology This study employs a Systematic Literature Review (SLR) protocol in the field of software engineering or related domains, which consists of three main phases: planning the review, conducting the review with a snowballing ap proach, and reporting the review. Furthermore, the authors perform open coding to categorize challenges based on the Agile methodologies adoption factor model and axial coding to map potential solutions. Contribution The authors assert that this research enriches the existing literature on Agile RE, specifically within the public sector context, by mapping out challenges and possible solutions that contribute to creating a foundation for future studies to conduct a more in-depth analysis of Agile adoption in the public sector. Furthermore, it compares the barriers of Agile RE in the public sector with the general context, leading to the discovery of new theories specifically for this field. Findings Most challenges related to Agile RE in the public sector are found in the people and process aspects. Project and organizational-related are subsequent aspects. Therefore, handling people and processes proficiently is imperative within Agile RE to prevent project failure. Recommendations for Practitioners Our findings offer a comprehensive view of Agile RE in the public sector in republican and constitutional monarchy nations. This study maps the challenges encountered by the public sector and provides potential solutions. The authors encourage practitioners to consider our findings as a foundation for adopting Agile methodology in the public sector. Furthermore, this study can assist practitioners in identifying existing barriers related to Agile RE, pinpointing elements that contribute to overcoming those challenges, and developing strategies based on the specific needs of the organizations. Recommendations for Researchers Researchers have the potential to expand the scope of this study by conducting research in other countries, especially African countries, as this study has not yet encompassed this geographic region. Additionally, they can strengthen the evidence linking Agile RE challenges to the risk of Agile project failure by performing empirical validation in a specific country. Impact on Society This research conducts a comprehensive exploration of Agile RE within the public sector, serving as a foundation for the successful adoption of Agile methodology by overcoming obstacles related to Agile RE. This study highlights the importance of managing people, processes, projects, and organizational elements to increase the success of Agile adoption in the public sector. Future Research In the future, researchers should work towards resolving the limitations identified in this study. This study has not provided a clear prioritization of challenges and solutions according to their significance. Therefore, future researchers can perform a Fuzzy Analytical Hierarchical Process (F-AHP) to prioritize the proposed solutions. [ABSTRACT FROM AUTHOR]

Fuzz testing, also known as fuzzing, is a software testing technique aimed at identifying software vulnerabilities. In recent decades, fuzzing has gained increasing popularity in the research community. However, existing studies led by fuzzing experts mainly focus on improving the coverage and performance of fuzzing techniques. That is, there is still a gap in empirical knowledge regarding fuzzing, especially about the challenges developers face when they adopt fuzzing. Understanding these challenges can provide valuable insights to both practitioners and researchers on how to further improve fuzzing processes and techniques. We conducted a study to understand the challenges encountered by developers during fuzzing. More specifically, we first manually analyzed 829 randomly sampled fuzzing-related GitHub issues and constructed a taxonomy consisting of 39 types of challenges (22 related to the fuzzing process itself, 17 related to using external fuzzing providers). We then surveyed 106 fuzzing practitioners to verify the validity of our taxonomy and collected feedback on how the fuzzing process can be improved. Our taxonomy, accompanied with representative examples and highlighted implications, can serve as a reference point on how to better adopt fuzzing techniques for practitioners, and indicates potential directions researchers can work on toward better fuzzing approaches and practices. [ABSTRACT FROM AUTHOR]

Accessible design focuses on enabling as many people as possible to access software products and services, which has drawn extensive attention from software engineering researchers and developers. Currently, developers are improving the accessibility of software in a simple way, and researchers are putting much effort into investigating the characteristics of accessibility issues. No studies have explored the interaction of accessibility with other factors that affect software performance, although some researchers have found that these accessibility problems interact with other quality attributes (e.g., functionality , efficiency , and security) when they categorize accessibility issues. To fill this gap, we conducted the first comprehensive study to characterize symptoms, the violated standard, causes, and solutions for the accessibility issues along with quality attributes of software with 4,572 accessibility issues collected from GitHub. Specifically, we first investigated the symptoms of the interaction between the accessibility issues and software quality attributes. Second, we manually assessed accessibility issues where the interaction occurs to determine the violated standards. We then analyzed the causes of these interaction problems. Finally, we explored the commits that fix these accessibility issues along with quality attributes to find solutions. Our results indicate that: i) usability and functionality are the quality attributes that most frequently interact with accessibility, appearing more than 66% of the time; ii) 87% of the interaction issues violated Perceivable and Operable in the Web Content Accessibility Guidelines; iii) the main reason for these interaction problems is that UI Component Related (UCR) and Contrast Related (CR) issues show up more than 52% of the time. • Study the relationship between accessibility and attributes. • Summary how developers address accessibility problems. • Create a reliable dataset. [ABSTRACT FROM AUTHOR]