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Failure to account for human values in software (e.g., equality and fairness) can result in user dissatisfaction and negative socioeconomic impact. Engineering these values in software, however, requires technical and methodological support throughout the development life cycle. This paper investigates to what extent top Software Engineering (SE) conferences and journals have included research on human values in SE. We investigate the prevalence of human values in recent (2015 - 2018) publications in these top venues. We classify these publications, based on their relevance to di erent values, against a widely used value structure adopted from the social sciences. Our results show that: (a) only a small proportion of the publications directly consider values, classified as directly relevant publications; (b) for the majority of the values, very few or no directly relevant publications were found; and (c) the prevalence of directly relevant publications was higher in SE conferences compared to SE journals. This paper shares these and other insights that may motivate future research on human values in software engineering. [ABSTRACT FROM AUTHOR]

One of the challenges end-users face in order to maintain and enhance their IT infrastructures is the simplicity and ease of use of the configuration/design-time interfaces. In this paper we describe our approach of using existing technologies for paper-based interfaces to allow end-users to orchestrate services in a Service-Oriented Architecture (SOA). We gathered first experiences with paper-based interfaces for IT configuration by developing an end-user-developable paper-based remote interface for interactive TV applications. Further we conducted a workshop to evaluate how end-user can orchestrate services. Here, we discuss the usefulness of a paper-based interface for the orchestration of services, from an end-user perspective and how the experiences from our prototype and the workshop can inform the design of a paper-based orchestration prototype for SOA. [ABSTRACT FROM AUTHOR]

Improper Input Validation (IIV) is a software vulnerability that occurs when a system does not safely handle input data. Even though IIV is easy to detect and fix, it still commonly happens in practice. In this paper, we study to what extent developers can detect IIV and investigate underlying reasons. This knowledge is essential to better understand how to support developers in creating secure software systems. We conduct an online experiment with 146 participants, of which 105 report at least three years of professional software development experience. Our results show that the existence of a visible attack scenario facilitates the detection of IIV vulnerabilities and that a significant portion of developers who did not find the vulnerability initially could identify it when warned about its existence. Yet, a total of 60 participants could not detect the vulnerability even after the warning. Other factors, such as the frequency with which the participants perform code reviews, influence the detection of IIV. [ABSTRACT FROM AUTHOR]

Our community constantly pushes the state-of-the-art by introducing "new" techniques. These techniques often build on top of, and are compared against, existing systems that realize previously published techniques. The underlying assumption is that existing systems correctly represent the techniques they implement. This paper examines that assumption through a study of KLEE, a popular and well-cited tool in our community. We briefly describe six improvements we made to KLEE, none of which can be considered "new" techniques, that provide order-of-magnitude performance gains. Given these improvements, we then investigate how the results and conclusions of a sample of papers that cite KLEE are affected. Our findings indicate that the strong emphasis on introducing "new" techniques may lead to wasted effort, missed opportunities for progress, an accretion of artifact complexity, and questionable research conclusions (in our study, 27% of the papers that depend on KLEE can be questioned). We conclude by revisiting initiatives that may help to realign the incentives to better support the foundations on which we build. [ABSTRACT FROM AUTHOR]

With the goal of helping software engineering researchers understand how to improve their papers, Mary Shaw presented "Writing Good Software Engineering Research Papers" in 2003. Shaw analyzed the abstracts of the papers submitted to the 2002 International Conference of Software Engineering (ICSE) to determine trends in research question type, contribution type, and validation approach. We revisit Shaw's work to see how the software engineering research community has evolved since 2002. The goal of this paper is to aid software engineering researchers in understanding trends in research question design, research question type, and validation approach by analyzing the abstracts of the papers submitted to ICSE 2016. We implemented Shaw's recommendation for replicating her study through the use of multiple coders and the calculation of inter-rater reliability and demonstrate that her approach can be repeated. Our results indicate that reviewers have increased expectations that papers have solid evaluations of the research contribution. Additionally, the 2016 results include at least 17% mining software repository (MSR) papers, a category of papers not seen in 2002. The advent of MSR papers has increased the use of generalization/ characterization research questions, the production of empirical report contribution, and validation by evaluation. [ABSTRACT FROM AUTHOR]

(IS) requires that the security concerns should be properly articulated well ahead in early requirement engineering (RE) along with other functional and non-functional requirements. In this paper, based on the domain model for IS security risk management (SRM) we propose a set of transformation guidelines to translate Secure Tropos models to the misuse case diagrams. We believe that such a model translation would help developers to elicit real security needs by integrating the security analysis starting from early requirement stages to all the stages of development process. The translation aligns the IS security concerns with functional requirements and maintains traceability of the security decisions to their origin. [ABSTRACT FROM AUTHOR]

The Third Workshop on Automation of Software Test (AST 2008) at the 30th International Conference on Software Engineering (ICSE 2008) sets a special theme on model-based software testing. Nine full research papers and six short papers will be presented in four sessions at the workshop. This report summarizes the organization of the workshop as well as the sessions and papers to be presented at the workshop. [ABSTRACT FROM AUTHOR]

Welcome to the Second International Workshop on Emerging Trends in Software Metrics (WETSoM 2011), held in Honolulu, Hawaii, as satellite workshop of the 33rd ACM/IEEE International Conference on Software Engineering (ICSE 2011). The workshop aims at gathering together researchers and practitioners to discuss the progress of software metrics. This year we received 26 papers by 70 unique authors from 14 countries. Every paper was reviewed by members of an international programme committee. We accepted 9 full papers (of 20 submitted) and 6 short papers (4 of the 6 submitted as short papers, and two long papers accepted as short papers). The program will feature technical sessions on (i) Software quality, (ii) Changes and defects, and (iii) challenges on software metrics, the latter followed by a working discussion on the topic. Last, but not least, we are honored to host a keynote by Professor Martin Shepperd, Brunel University, UK, on "Data Quality: Cinderella at the Software Metrics Ball". We would like to thank the authors and reviewers for their time and effort in providing a quality programme. Also, we would like to thank the ICSE 2011 organization for making this event possible, and the ICSE workshop chairs for proactively guiding us in the workshop organization process. [ABSTRACT FROM AUTHOR]

The peer review process is central to the scientific method, the advancement and spread of research, as well as crucial for individual careers. However, the single-blind review mode currently used in most Software Engineering (SE) venues is susceptible to apparent and hidden biases, since reviewers know the identity of authors. We perform a study on the benefits and costs that are associated with introducing doubleblind review in SE venues. We surveyed the SE community's opinion and interviewed experts on double-blind reviewing. Our results indicate that the costs, mostly logistic challenges and side effects, outnumber its benefits and mostly regard difficulty for authors in blinding papers, for reviewers in understanding the increment with respect to previous work from the same authors, and for organizers to manage a complex transition. While the surveyed community largely consents on the costs of DBR, only less than one-third disagree with a switch to DBR for SE journals, all SE conferences, and, in particular, ICSE; the analysis of a survey with authors of submitted papers at ICSE 2016 run by the program chairs of that edition corroborates our result. [ABSTRACT FROM AUTHOR]

Deep learning (DL) techniques are gaining more and more attention in the software engineering community. They have been used to support several code-related tasks, such as automatic bug fixing and code comments generation. Recent studies in the Natural Language Processing (NLP) field have shown that the Text-To-Text Transfer Transformer (T5) architecture can achieve state-of-the-art performance for a variety of NLP tasks. The basic idea behind T5 is to first pre-train a model on a large and generic dataset using a self-supervised task (e.g., filling masked words in sentences). Once the model is pre-trained, it is fine-tuned on smaller and specialized datasets, each one related to a specific task (e.g., language translation, sentence classification). In this paper, we empirically investigate how the T5 model performs when pre-trained and fine-tuned to support code-related tasks. We pre-train a T5 model on a dataset composed of natural language English text and source code. Then, we fine-tune such a model by reusing datasets used in four previous works that used DL techniques to: (i) fix bugs, (ii) inject code mutants, (iii) generate assert statements, and (iv) generate code comments. We compared the performance of this single model with the results reported in the four original papers proposing DL-based solutions for those four tasks. We show that our T5 model, exploiting additional data for the self-supervised pre-training phase, can achieve performance improvements over the four baselines. [ABSTRACT FROM AUTHOR]

Deep learning models, like traditional software systems, provide a large number of configuration options. A deep learning model can be configured with different hyperparameters and neural architectures. Recently, AutoML (Automated Machine Learning) has been widely adopted to automate model training by systematically exploring diverse configurations. However, current AutoML approaches do not take into consideration the computational constraints imposed by various resources such as available memory, computing power of devices, or execution time. The training with non-conforming configurations could lead to many failed AutoML trial jobs or inappropriate models, which cause significant resource waste and severely slow down development productivity. In this paper, we propose DnnSAT, a resource-guided AutoML approach for deep learning models to help existing AutoML tools efficiently reduce the configuration space ahead of time. DnnSAT can speed up the search process and achieve equal or even better model learning performance because it excludes trial jobs not satisfying the constraints and saves resources for more trials. We formulate the resource-guided configuration space reduction as a constraint satisfaction problem. DnnSAT includes a unified analytic cost model to construct common constraints with respect to the model weight size, number of floating-point operations, model inference time, and GPU memory consumption. It then utilizes an SMT solver to obtain the satisfiable configurations of hyperparameters and neural architectures. Our evaluation results demonstrate the effectiveness of DnnSAT in accelerating stateof- the-art AutoML methods (Hyperparameter Optimization and Neural Architecture Search) with an average speedup from 1.19X to 3.95X on public benchmarks. We believe that DnnSAT can make AutoML more practical in a real-world environment with constrained resources. [ABSTRACT FROM AUTHOR]

Code prediction, more specifically autocomplete, has become an essential feature in modern IDEs. Autocomplete is more effective when the desired next token is at (or close to) the top of the list of potential completions offered by the IDE at cursor position. This is where the strength of the underlying machine learning system that produces a ranked order of potential completions comes into play. We advance the state-of-the-art in the accuracy of code prediction (next token prediction) used in autocomplete systems. Our work uses Transformers as the base neural architecture. We show that by making the Transformer architecture aware of the syntactic structure of code, we increase the margin by which a Transformer-based system outperforms previous systems. With this, it outperforms the accuracy of several state-of-the-art next token prediction systems by margins ranging from 14% to 18%. We present in the paper several ways of communicating the code structure to the Transformer, which is fundamentally built for processing sequence data. We provide a comprehensive experimental evaluation of our proposal, along with alternative design choices, on a standard Python dataset, as well as on a company internal Python corpus. Our code and data preparation pipeline will be available in open source. [ABSTRACT FROM AUTHOR]

We would like to welcome you to this, the first workshop on Developing Tools as Plug-Ins (TOPI 2011). Tools and environments to increase software quality and productivity have always been an important aspect of software engineering. There are two major ways for programmers to communicate with the computer - programming languages and tools. Through languages, programmers encode decisions, actions and algorithms, while tools have the added advantage of providing analysis, synthesis and visualization. At the same time, software development environments have grown in power: today's environments, such as Visual Studio, Eclipse or an Emacs configuration, bear little resemblance to those of two decades ago. Syntactic and documentation support, recommendations for fixes and re-factorings, relatively seamless integration with version control and debugging tools, and more, are not only available, but are expected by modern programmers. Plug-ins are the modern approach towards incrementally adding new tools into these rich environments. Proving for plug-ins is necessary because it is at best very hard to get programmers to try a new tool without integrating it into an existing environment. Indeed, our knowledge of how to solve software engineering problems is increasingly being encapsulated in tools. These tools are at their strongest when they operate in a pre-existing development environment. This approach allows integration with existing elements such as compilers, debuggers, profilers, visualizers as well as numerous other development and, often, runtime tools. However, building tools as plug-ins can be challenging. How do they interact with the core environment? How do they interact with one another - especially since each developer may choose a different set of plug-ins? How can we share tools across different, and future, core development environments? These are all questions that will be addressed during TOPI 2011. The workshop received 32 submissions from 21 countries. They attested to the interest in the topic and to the diversity of IDEs currently in use today, including Visual Studio 2010, Eclipse and MonoDevelop. Each paper was reviewed by three member of the PC, and then there followed a vigorous online meeting. From this, we selected 14 full papers and 8 short papers, giving an acceptance rate of 68%. Through these papers, we shall address the following themes: *identify recent successful tools as plug-ins *categorize the characteristics of good plug-ins *understand interoperability requirements to making tools available across platforms *list which tools lend themselves best to the plug-in approach *specify the medium and long term challenges of tools as plug-ins So, while we are more concerned in this workshop with understanding the characteristics and creation of tools as plug-ins, than of the tools themselves, we did accept some papers with very interesting tools, including those from industry. The workshop will be opened with a welcoming session by the chairs, followed by a keynote by Tom Ball from Microsoft Research on the Software Engineering of Plug-in Architectures. The talk will be punctuated by personal insights and Tom's long experience as a research leader. In the following two sessions, selected papers describing successful plug-in experience and practice will be presented. We want to try and identify patterns and commonalities that could be used to characterize "good" plug-ins. The goal was to come out with a first version of a framework that could be used to classify plug-in tools. During the third session we invited a discussion around presentations that tackled the barriers of making plug-ins available across different platforms. Rather than arguing for an agnostic approach, the goal is to better understand the differences and identify opportunities to interact and cooperate. The last session of the workshop is dedicated to presentations that look into the future of tools as plug-ins, followed by a brainstorming session on medium and long term challenges of plug-in development. The papers to be presented at TOPI 2011 indicate that developing better ways to plug tools into modern environments, and developing better ways for those tools to interact with other related tools in these environments, is definitively a critical research topic. [ABSTRACT FROM AUTHOR]

This paper is a report on The 8th IEEE/ACM International Workshop on Automation of Software Test (AST 2013) at the 35th International Conference on Software Engineering (ICSE 2013). It sets a special theme on testing-as-a-service (TaaS). Keynote speech and charette discussions are organized around this special theme. Eighteen full research papers and six short papers will be presented in the two-day workshop. The report will give the background of the workshop and the selection of the special theme, and report on the organization of the workshop. The provisional program will be presented with a list of the sessions and papers to be presented at the workshop. [ABSTRACT FROM AUTHOR]

The engineering of high-quality software requirements generally relies on properties and assumptions about the environment in which the software-to-be has to operate. Such properties and assumptions, referred to as environment conditions in this paper, are highly subject to change over time or from one software variant to another. As a consequence, the requirements engineered for a specific set of environment conditions may no longer be adequate, complete and consistent for another set. The paper addresses this problem through a tool-supported requirements adaptation technique. A goal-oriented requirements modelling framework is considered to make requirements' refinements and dependencies on environment conditions explicit. When environment conditions change, an adapted goal model is computed that is correct with respect to the new environment conditions. The space of possible adaptations is not fixed a priori; the required changes are expected to meet one or more environment-independent goal(s) to be satisfied in any version of the system. The adapted goal model is generated using a new counterexample-guided learning procedure that ensures the correctness of the updated goal model, and prefers more local adaptations and more similar goal models. [ABSTRACT FROM AUTHOR]

Context: Following on other scientific disciplines, such as health sciences, the use of Grey Literature (GL) has become widespread in Software Engineering (SE) research. Whilst the number of papers incorporating GL in SE is increasing, there is little empirically known about different aspects of the use of GL in SE research. Method: We used a mixed-methods approach for this research. We carried out a Systematic Literature Review (SLR) of the use of GL in SE, and surveyed the authors of the selected papers included in the SLR (as GL users) and the invited experts in SE community on the use of GL in SE research. Results: We systematically selected and reviewed 102 SE secondary studies that incorporate GL in SE research, from which we identified two groups based on their reporting: 1) 76 reviews only claim their use of GL; 2) 26 reviews report the results by including GL.We also obtained 20 replies from the GL users and 24 replies from the invited SE experts. Conclusion: There is no common understanding of the meaning of GL in SE. Researchers define the scopes and the definitions of GL in a variety of ways.We found five main reasons of using GL in SE research. The findings have enabled us to propose a conceptual model for how GL works in SE research lifecycle. There is an apparent need for research to develop guidelines for using GL in SE and for assessing quality of GL. The current work can provide a panorama of the state-of-the-art of using GL in SE for the follow-up research, as to determine the important position of GL in SE research. [ABSTRACT FROM AUTHOR]

Using topic modeling, we analyse the titles and abstracts of nearly 10,000 papers from 20 years published in 11 top-ranked Software Engineering(SE) conferences between 1993 to 2013. Seven topics are identified as the dominant themes in modern software engineering. We show that these topics are not static; rather, some of them are becoming decidedly less prominent over time (modeling) while others are become very prominent indeed (defect analysis). By clustering conferences according to the topics they publish, we identify four large groups of SE conferences; e.g. ASE, FSE and ICSE publish mostly the same work (exceptions: there are more program analysis results in FSE than in ASE or ICSE). Using these results, we offer numerous recommendations including how to plan an individuals research program; when to make or merge conferences; and how to encourage a broader range of topics at SE conferences. An extended version of this paper, that analyzes more conferences and papers, is available on https://goo.gl/mVdyfj. [ABSTRACT FROM AUTHOR]

Source code summarization -- creating natural language descriptions of source code behavior -- is a rapidly-growing research topic with applications to automatic documentation generation, program comprehension, and software maintenance. Traditional techniques relied on heuristics and templates built manually by human experts. Recently, data-driven approaches based on neural machine translation have largely overtaken template-based systems. But nearly all of these techniques rely almost entirely on programs having good internal documentation; without clear identifier names, the models fail to create good summaries. In this paper, we present a neural model that combines words from code with code structure from an AST. Unlike previous approaches, our model processes each data source as a separate input, which allows the model to learn code structure independent of the text in code. This process helps our approach provide coherent summaries in many cases even when zero internal documentation is provided. We evaluate our technique with a dataset we created from 2.1m Java methods. We find improvement over two baseline techniques from SE literature and one from NLP literature. [ABSTRACT FROM AUTHOR]

Program verification is one of the most important methods to ensuring the correctness of concurrent programs. However, due to the path explosion problem, concurrent program verification is usually time consuming, which hinders its scalability to industrial programs. Parallel processing is a mainstream technique to deal with those problems which require mass computing. Hence, designing parallel algorithms to improve the performance of concurrent program verification is highly desired. This paper focuses on parallelization of the abstraction refinement technique, one of the most efficient techniques for concurrent program verification. We present a parallel refinement framework which employs multiple engines to refine the abstraction in parallel. Different from existing work which parallelizes the search process, our method achieves the effect of parallelization by refinement constraint and learnt clause sharing, so that the number of required iterations can be significantly reduced. We have implemented this framework on the scheduling constraint based abstraction refinement method, one of the best methods for concurrent program verification. Experiments on SV-COMP 2018 show the encouraging results of our method. For those complex programs requiring a large number of iterations, our method can obtain a linear reduction of the iteration number and significantly improve the verification performance. [ABSTRACT FROM AUTHOR]

This paper is a report on The 10th IEEE/ACM International Workshop on Automation of Software Test (AST 2015) at the 37th International Conference on Software Engineering (ICSE 2015). It sets a special theme on testing oracles. Keynote speeches and charette discussions are organized around this special theme. 16 full research papers and 2 keynotes will be presented in the two-day workshop. The report will give the background of the workshop and the selection of the special theme, and report on the organization of the workshop. The provisional program will be presented with a list of the sessions and papers to be presented at the workshop. [ABSTRACT FROM AUTHOR]

Limited by the small keyboard, most mobile apps support the automatic login feature for better user experience. Therefore, users avoid the inconvenience of retyping their ID and password when an app runs in the foreground again. However, this auto-login function can be exploited to launch the so-called "data-clone attack": once the locally-stored, auto-login depended data are cloned by attackers and placed into their own smartphones, attackers can break through the login-device number limit and log in to the victim's account stealthily. A natural countermeasure is to check the consistency of device-specific attributes. As long as the new device shows different device fingerprints with the previous one, the app will disable the auto-login function and thus prevent data-clone attacks. In this paper, we develop VPDroid, a transparent Android OS-level virtualization platform tailored for security testing. With VPDroid, security analysts can customize different device artifacts, such as CPU model, Android ID, and phone number, in a virtual phone without user-level API hooking. VPDroid's isolation mechanism ensures that user-mode apps in the virtual phone cannot detect device-specific discrepancies. To assess Android apps' susceptibility to the data-clone attack, we use VPDroid to simulate data-clone attacks with 234 most-downloaded apps. Our experiments on five different virtual phone environments show that VPDroid's device attribute customization can deceive all tested apps that perform device-consistency checks, such as Twitter, WeChat, and PayPal. 19 vendors have confirmed our report as a zero-day vulnerability. Our findings paint a cautionary tale: only enforcing a device-consistency check at client side is still vulnerable to an advanced data-clone attack. [ABSTRACT FROM AUTHOR]

Web accessibility, the design of web apps to be usable by users with disabilities, impacts millions of people around the globe. Although accessibility has traditionally been a marginal afterthought that is often ignored in many software products, it is increasingly becoming a legal requirement that must be satisfied. While some web accessibility testing tools exist, most only perform rudimentary syntactical checks that do not assess the more important high-level semantic aspects that users with disabilities rely on. Accordingly, assessing web accessibility has largely remained a laborious manual process requiring human input. In this paper, we propose an approach, called AXERAY, that infers semantic groupings of various regions of a web page and their semantic roles. We evaluate our approach on 30 real-world websites and assess the accuracy of semantic inference as well as the ability to detect accessibility failures. The results show that AXERAY achieves, on average, an F-measure of 87% for inferring semantic groupings, and is able to detect accessibility failures with 85% accuracy. [ABSTRACT FROM AUTHOR]

The boundary conditions (BCs) have shown great potential in requirements engineering because a BC captures the particular combination of circumstances, i.e., divergence, in which the goals of the requirement cannot be satisfied as a whole. Existing researches have attempted to automatically identify lots of BCs. Unfortunately, a large number of identified BCs make assessing and resolving divergences expensive. Existing methods adopt a coarse-grained metric, generality, to filter out less general BCs. However, the results still retain a large number of redundant BCs since a general BC potentially captures redundant circumstances that do not lead to a divergence. Furthermore, the likelihood of BC can be misled by redundant BCs resulting in costly repeatedly assessing and resolving divergences. In this paper, we present a fine-grained metric to filter out the redundant BCs. We first introduce the concept of contrasty of BC. Intuitively, if two BCs are contrastive, they capture different divergences. We argue that a set of contrastive BCs should be recommended to engineers, rather than a set of general BCs that potentially only indicates the same divergence. Then we design a post-processing framework (PPFc) to produce a set of contrastive BCs after identifying BCs. Experimental results show that the contrasty metric dramatically reduces the number of BCs recommended to engineers. Results also demonstrate that lots of BCs identified by the state-of-the-art method are redundant in most cases. Besides, to improve efficiency, we propose a joint framework (JFc) to interleave assessing based on the contrasty metric with identifying BCs. The primary intuition behind JFc is that it considers the search bias toward contrastive BCs during identifying BCs, thereby pruning the BCs capturing the same divergence. Experiments confirm the improvements of JFc in identifying contrastive BCs. [ABSTRACT FROM AUTHOR]

Machine learning software can be unfair when making human-related decisions, having prejudices over certain groups of people. Existing work primarily focuses on proposing fairness metrics and presenting fairness improvement approaches. It remains unclear how key aspect of any machine learning system, such as feature set and training data, affect fairness. This paper presents results from a comprehensive study that addresses this problem. We find that enlarging the feature set plays a significant role in fairness (with an average effect rate of 38%). Importantly, and contrary to widely-held beliefs that greater fairness often corresponds to lower accuracy, our findings reveal that an enlarged feature set has both higher accuracy and fairness. Perhaps also surprisingly, we find that a larger training data does not help to improve fairness. Our results suggest a larger training data set has more unfairness than a smaller one when feature sets are insufficient; an important cautionary finding for practising software engineers. [ABSTRACT FROM AUTHOR]

Statistical fault localization (SFL) techniques use execution profiles and success/failure information from software executions, in conjunction with statistical inference, to automatically score program elements based on how likely they are to be faulty. SFL techniques typically employ one type of profile data: either coverage data, predicate outcomes, or variable values. Most SFL techniques actually measure correlation, not causation, between profile values and success/failure, and so they are subject to confounding bias that distorts the scores they produce. This paper presents a new SFL technique, named UniVal, that uses causal inference techniques and machine learning to integrate information about both predicate outcomes and variable values to more accurately estimate the true failure-causing effect of program statements. UniVal was empirically compared to several coverage-based, predicate-based, and value-based SFL techniques on 800 program versions with real faults. [ABSTRACT FROM AUTHOR]

This paper describes the results of a large (+1100 responses) survey of professional software developers concerning standards for naming source code methods. The various standards for source code method names are derived from and supported in the software engineering literature. The goal of the survey is to determine if there is a general consensus among developers that the standards are accepted and used in practice. Additionally, the paper examines factors such as years of experience and programming language knowledge in the context of survey responses. The survey results show that participants very much agree about the importance of various standards and how they apply to names and that years of experience and the programming language has almost no effect on their responses. The results imply that the given standards are both valid and to a large degree complete. The work provides a foundation for automated method name assessment during development and code reviews. [ABSTRACT FROM AUTHOR]

Continuous integration (CI) is a widely used practice in modern software engineering. Unfortunately, it is also an expensive practice -- Google and Mozilla estimate their CI systems in millions of dollars. There are a number of techniques and tools designed to or having the potential to save the cost of CI or expand its benefit - reducing time to feedback. However, their benefits in some dimensions may also result in drawbacks in others. They may also be beneficial in other scenarios where they are not designed to help. In this paper, we perform the first exhaustive comparison of techniques to improve CI, evaluating 14 variants of 10 techniques using selection and prioritization strategies on build and test granularity. We evaluate their strengths and weaknesses with 10 different cost and time-to-feedback saving metrics on 100 real-world projects. We analyze the results of all techniques to understand the design decisions that helped different dimensions of benefit. We also synthesized those results to lay out a series of recommendations for the development of future research techniques to advance this area. [ABSTRACT FROM AUTHOR]

Combinatorial interaction testing (CIT) is an important technique for testing highly configurable software systems with demonstrated effectiveness in practice. The goal of CIT is to generate test cases covering the interactions of configuration options, under certain hard constraints. In this context, constrained covering arrays (CCAs) are frequently used as test cases in CIT. Constrained Covering Array Generation (CCAG) is an NP-hard combinatorial optimization problem, solving which requires an effective method for generating small CCAs. In particular, effectively solving t-way CCAG with t > 4 is even more challenging. Inspired by the success of automated algorithm configuration and automated algorithm selection in solving combinatorial optimization problems, in this paper, we investigate the efficacy of automated algorithm configuration and automated algorithm selection for the CCAG problem, and propose a novel, automated CCAG approach called AutoCCAG. Extensive experiments on public benchmarks show that AutoCCAG can find much smaller-sized CCAs than current state-of-the-art approaches, indicating the effectiveness of AutoCCAG. More encouragingly, to our best knowledge, our paper reports the first results for CCAG with a high coverage strength (i.e., 5-way CCAG) on public benchmarks. Our results demonstrate that AutoCCAG can bring considerable benefits in testing highly configurable software systems. [ABSTRACT FROM AUTHOR]