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Empirical research aims to establish generalizable claims from data. Such claims involve concepts that often must be measured indirectly by using indicators. Construct validity is concerned with whether one can justifiably make claims at the conceptual level that are supported by results at the operational level. We report a quantitative analysis of the awareness of construct validity in the software engineering literature between 2000 and 2019 and a qualitative review of 83 articles about human-centric experiments published in five high-quality journals between 2015 and 2019. Over the two decades, the appearance in the literature of the term construct validity increased sevenfold. Some of the reviewed articles we reviewed employed various ways to ensure that the indicators span the concept in an unbiased manner. We also found articles that reuse formerly validated constructs. However, the articles disagree about how to define construct validity. Several interpret construct validity excessively by including threats to internal, external, or statistical conclusion validity. A few articles also include fundamental challenges of a study, such as cheating and misunderstandings of experiment material. The diversity of topics discussed makes us recommend a minimalist approach to construct validity. We propose seven guidelines to establish a common ground for addressing construct validity in software engineering.

Existing programming practices for building Android apps mainly follow the one-size-fits-all strategy to include lots of functions and adapt to most types of devices. However, this strategy can result in software bloat and many serious issues, such as slow download speed, and large attack surfaces. Existing solutions cannot effectively debloat an app as they either lack flexibility or require human efforts. This work proposes a novel feature-oriented debloating approach and builds a new tool, named XDebloat, to automate this process in a flexible manner. First, XDebloat supports feature location approaches at a fine granularity. It also makes the feature location results editable and changeable. Second, XDebloat considers several Android-oriented issues (i.e., callback, UI dependencies), which are not covered in the state-of-art approaches. Third, XDebloat support two major debloating strategies: pruning-based and module-based debloating. We evaluate XDebloat with 200 open-source and 1,000 commercial apps. The results show that XDebloat can successfully remove unwanted features from apps or transform them into on-demand modules within 10 minutes. For the pruning-based debloating strategy, on average, XDebloat can remove 32.1% code from an app. For the module-based debloating strategy, XDebloat can help developers build modules automatically.

Grounded Theory (GT), a sociological research method designed to study social phenomena, is increasingly being used to investigate the human and social aspects of software engineering (SE). However, being written by and for sociologists, GT is often challenging for a majority of SE researchers to understand and apply. Additionally, SE researchers attempting ad hoc adaptations of traditional GT guidelines for modern socio-technical (ST) contexts often struggle in the absence of clear and relevant guidelines to do so, resulting in poor quality studies. To overcome these research community challenges and leverage modern research opportunities, this paper presents Socio-Technical Grounded Theory (STGT) designed to ease application and achieve quality outcomes. It defines what exactly is meant by an ST research context and presents the STGT guidelines that expand GT's philosophical foundations, provide increased clarity and flexibility in its methodological steps and procedures, define possible scope and contexts of application, encourage frequent reporting of a variety of interim, preliminary, and mature outcomes, and introduce nuanced evaluation guidelines for different outcomes. It is hoped that the SE research community and related ST disciplines such as computer science, data science, artificial intelligence, information systems, human computer/robot/AI interaction, human-centered emerging technologies (and increasingly other disciplines being transformed by rapid digitalisation and AI-based augmentation), will benefit from applying STGT to conduct quality research studies and systematically produce rich findings and mature theories with confidence., Comment: Accepted to IEEE Transactions on Software Engineering (Aug 2021); This author preprint is 26 pages, the final IEEE version is 25 pages due to reformatting, content is same. More details: https://rashina.com/

On-board embedded software developed for spaceflight systems (space software) must adhere to stringent software quality assurance procedures. For example, verification and validation activities are typically performed and assessed by third party organizations. To further minimize the risk of human mistakes, space agencies, such as the European Space Agency (ESA), are looking for automated solutions for the assessment of software testing activities, which play a crucial role in this context. Over the years, mutation analysis has shown to be a promising solution for the automated assessment of test suites; it consists of measuring the quality of a test suite in terms of the percentage of injected faults leading to a test failure. A number of optimization techniques, addressing scalability and accuracy problems, have been proposed to facilitate the industrial adoption of mutation analysis. However, to date, two major problems prevent space agencies from enforcing mutation analysis in space software development. In this paper, we enhance mutation analysis optimization techniques to enable their applicability to embedded software and propose a pipeline that successfully integrates them to address scalability and accuracy issues in this context, as described above. Further, we report on the largest study involving embedded software systems in the mutation analysis literature. Our research is part of a research project funded by ESA ESTEC involving private companies (GomSpace Luxembourg and LuxSpace) in the space sector. These industry partners provided the case studies reported in this paper; they include an on-board software system managing a microsatellite currently on-orbit, a set of libraries used in deployed cubesats, and a mathematical library certified by ESA., Accepted for publication on IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

Application Programming Interfaces (APIs) allow their users to reuse existing software functionality without implementing it by themselves. However, using external functionality can come at a cost. Because developers are decoupled from the API's inner workings, they face the possibility of misunderstanding, and therefore misusing APIs. Prior research has proposed state-of-the-art example-based API misuse detectors that rely on existing API usage examples mined from existing code bases. Intuitively, without a varied dataset of API usage examples, it is challenging for the example-based API misuse detectors to differentiate between infrequent but correct API usages and API misuses. Such mistakes lead to false positives in the API misuse detection results, which was reported in a recent study as a major limitation of the state-of-the-art. To tackle this challenge, in this paper, we first undertake a qualitative study of 384 falsely detected API misuses. We find that around one third of the false-positives are due to missing alternative correct API usage examples. Based on the knowledge gained from the qualitative study, we uncover five patterns which can be followed to generate artificial examples for complementing existing API usage examples in the API misuse detection. To evaluate the usefulness of the generated artificial examples, we apply a state-of-the-art example-based API misuse detector on 50 open source Java projects. We find that our artificial examples can complement the existing API usage examples by preventing the detection of 55 false API misuses. Furthermore, we conduct a pre-designed experiment in an automated API misuse detection benchmark (MUBench), in order to evaluate the impact of generated artificial examples on recall. We find that the API misuse detector covers the same true positive results with and without the artificial example, i.e., obtains the same recall of 94.7%. Our findings highlight the potential of improving API misuse detection by pattern-guided source code transformation techniques.

Software projects capture information in various kinds of artifacts, including source code, tests, and documentation. Such artifacts routinely encode information that is redundant, i.e., when a specification encoded in the source code is also separately tested and documented. Without supporting technology, such redundancy easily leads to inconsistencies and a degradation of documentation quality. We designed a tool-supported technique, called DScribe, that leverages redundancy between tests and documentation to generate consistent and checkable documentation and unit tests based on a single source of information. DScribe generates unit tests and documentation fragments based on a novel template and artifact generation technology. By pairing tests and documentation generation, DScribe provides a mechanism to automatically detect and replace outdated documentation. Our evaluation of the Apache Commons IO library revealed that of 835 specifications about exception handling, 85% of them were not tested or correctly documented, and DScribe could be used to automatically generate 97% of the tests and documentation.

Internet of Things based systems (IoT systems for short) are becoming increasingly popular across different industrial domains and their development is rapidly increasing to provide value-added services to end-users and citizens. Little research to date uncovers the core development process lifecycle needed for IoT systems, and thus software engineers find themselves unprepared and unfamiliar with this new genre of system development. To ameliorate this gap, we conducted a mixed quantitative and qualitative research study where we derived a conceptual process framework from the extant literature on IoT, through which 27 key tasks for incorporation into the development processes of IoT systems were identified. The framework was then validated by means of a survey of 127 IoT practitioners from 35 countries across 6 continents with 15 different industry backgrounds. Our research provides an understanding of the most important development process tasks and informs both software engineering practitioners and researchers of the challenges and recommendations related to the development of next-generation of IoT systems.

In recent years, Python has experienced explosive growth in adoption, particularly among open source projects. While Python's dynamically-typed nature provides developers with powerful programming abstractions, that same dynamic type system allows for type-related defects to accumulate in code bases. To aid in the early detection of type-related defects, type annotations were introduced into the Python ecosystem (i.e., PEP-484) and static type checkers like mypy have appeared on the market. While applying a type checker like mypy can in theory help to catch type-related defects before they impact users, little is known about the real impact of adopting a type checker to reveal defects in Python projects. In this paper, we study the extent to which Python projects benefit from such type checking features. For this purpose, we mine the issue tracking and version control repositories of 210 Python projects on GitHub. Inspired by the work of Gao et al. on type-related defects in JavaScript, we add type annotations to test whether detects an error that would have helped developers to avoid real defects. We observe that 15% of the defects could have been prevented by mypy. Moreover, we find that there is no significant difference between the experience level of developers committing type-related defects and the experience of developers committing defects that are not type-related. In addition, a manual analysis of the anti-patterns that most commonly lead to type-checking faults reveals that the redefinition of Python references, dynamic attribute initialization and incorrectly handled Null objects are the most common causes of type-related faults. Since our study is conducted on fixed public defects that have gone through code reviews and multiple test cycles, these results represent a lower bound on the benefits of adopting a type checker. Therefore, we recommend incorporating a static type checker like mypy into the development workflow, as not only will it prevent type-related defects but also mitigate certain anti-patterns during development.

As a software system evolves, its architecture tends to decay, leading to the occurrence of architectural elements that become resistant to maintenance or prone to defects. To address this problem, engineers can significantly benefit from determining which architectural elements will decay before that decay actually occurs. Forecasting decay allows engineers to take steps to prevent decay, such as focusing maintenance resources on the architectural elements most likely to decay. To that end, we construct novel models that predict the quality of an architectural element by utilizing multiple architectural views (both structural and semantic) and architectural metrics as features for prediction. We conduct an empirical study using our prediction models on 38 versions of five systems. Our findings show that we can predict low architectural quality, i.e., architectural decay, with high performanceeven for cases of decay that suddenly occur in an architectural module. We further report the factors that best predict architectural quality.

In 2009, Moody introduced nine principles for evaluating, improving and designing cognitively effective notations called the Physics of Notations [49] motivating many research works ever since, being cited more than 1250 times at the time of writing this paper. Many research works have adopted the nine principles of the Physics of Notations to improve existing notations or devise new notations. Modeling is a two-step process that has the goal of communicating a mental concept by a model constructor (step one) to a model reader (step two). A subset of the research works utilizing the Physics of Notations have empirically validated the cognitive effectiveness of the new notations by their readers. However, there lacks any empirical evidence that investigates the effect of using Physics of Notations-enabled notations in model construction. This is a serious matter to be investigated as naturally model construction preludes model comprehension. Poorly constructed models can at best be poorly comprehended by its readers having dire consequences in downstream development activities. This paper reports on three different experiments that use software engineering professionals as subjects. The experiments investigate the effect of using notations that adhere to the Physics if Notations principles on model construction efforts. The results do not indicate an outright advantage for model constructors who utilize Physics of Notations-enabled notations in comparison to using their original versions of these notations.

API misuses are well-known causes of software crashes and security vulnerabilities. However, their detection and repair is challenging given that the correct usages of (third-party) APIs might be obscure to the developers of client programs. This paper presents the first empirical study to assess the ability of existing automated bug repair tools to repair API misuses, which is a class of bugs previously unexplored. Our study examines and compares 14 Java test-suite-based repair tools (11 proposed before 2018, and three afterwards) on a manually curated benchmark (APIREPBENCH) consisting of 101 API misuses. We develop an extensible execution framework (APIARTY) to automatically execute multiple repair tools. Our results show that the repair tools are able to generate patches for 28% of the API misuses considered. While the 11 less recent tools are generally fast (the median execution time of the repair attempts is 3.87 minutes and the mean execution time is 30.79 minutes), the three most recent are less efficient (i.e., 98% slower) than their predecessors. The tools generate patches for API misuses that mostly belong to the categories of missing null check, missing value, missing exception, and missing call. Most of the patches generated by all tools are plausible (65%), but only few of these patches are semantically correct to human patches (25%). Our findings suggest that the design of future repair tools should support the localisation of complex bugs, including different categories of API misuses, handling of timeout issues, and ability to configure large software projects. Both APIREPBENCH and APIARTY have been made publicly available for other researchers to evaluate the capabilities of repair tools on detecting and fixing API misuses.

Software engineering (SE) research has traditionally revolved around engineering the source code. However, novel approaches that analyze software through computer vision have been increasingly adopted in SE. These approaches allow analyzing the software from a different complementary perspective other than the source code, and they are used to either complement existing source code-based methods, or to overcome their limitations. The goal of this manuscript is to survey the use of computer vision techniques in SE with the aim of assessing their potential in advancing the field of SE research. We examined an extensive body of literature from top-tier SE venues, as well as venues from closely related fields (machine learning, computer vision, and human-computer interaction). Our inclusion criteria targeted papers applying computer vision techniques that address problems related to any area of SE. We collected an initial pool of 2,716 papers, from which we obtained 66 final relevant papers covering a variety of SE areas. We analyzed what computer vision techniques have been adopted or designed, for what reasons, how they are used, what benefits they provide, and how they are evaluated. Our findings highlight that visual approaches have been adopted in a wide variety of SE tasks, predominantly for effectively tackling software analysis and testing challenges in the web and mobile domains. The results also show a rapid growth trend of the use of computer vision techniques in SE research.

Due to the conflicting nature of quality measures, there are always multiple refactoring options to fix quality issues. Thus, interaction with developers is critical to inject their preferences. While several interactive techniques have been proposed, developers still need to examine large numbers of possible refactorings, which makes the interaction time-consuming. Furthermore, existing interactive tools are limited to the "objective space" to show developers the impacts of refactorings on quality attributes. However, the "decision space" is also important since developers may want to focus on specific code locations. In this paper, we propose an interactive approach that enables developers to pinpoint their preference simultaneously in the objective (quality metrics) and decision (code location) spaces. Developers may be interested in looking at refactoring strategies that can improve a specific quality attribute, such as extendibility (objective space), but they are related to different code locations (decision space). A plethora of solutions is generated at first using multi-objective search that tries to find the possible trade-offs between quality objectives. Then, an unsupervised learning algorithm clusters the trade-off solutions based on their quality metrics, and another clustering algorithm is applied to each cluster of the objective space to identify solutions related to different code locations. The objective and decision spaces can now be explored more efficiently by the developer, who can give feedback on a smaller number of solutions. This feedback is then used to generate constraints for the optimization process, to focus on the developer's regions of interest in both the decision and objective spaces. The manual validation of selected refactoring solutions by developers confirms that our approach outperforms state of the art refactoring techniques.

Software systems evolve throughout their life cycles. Many revisions are produced over time. Verifying each revision of the software is impractical. Regression verification suggests reusing intermediate results from the previous verification runs. This paper studies regression verification via summary reuse. Not only procedure summaries, but also loop summaries are proposed to be reused. This paper proposes a fully automatic regression verification technique in the context of CEGAR. A lazy counterexample analysis technique is developed to improve the efficiency of summary reuse. We performed extensive experiments on two large sets of industrial programs (3,675 revisions of 488 Linux kernel device drivers). Results show that our summary reuse technique saves 84% to 93% analysis time of the regression verification.

As increasingly complex software is developed every day, a growing number of companies use static analysis tools to reason about program properties ranging from simple coding style rules to more advanced software bugs, to multi-tier security vulnerabilities. While increasingly complex analyses are created, developer support must also be updated to ensure that the tools are used to their best potential. Past research in the usability of static analysis tools has primarily focused on usability issues encountered by software developers, and the causes of those issues in analysis tools. In this article, we adopt a more user-centered approach, and aim at understanding why software developers use analysis tools, which decisions they make when using those tools, what they look for when making those decisions, and the motivation behind their strategies. This approach allows us to derive new tool requirements that closely support software developers (e.g., systems for recommending warnings to fix that take developer knowledge into account), and also open novel avenues for further static-analysis research such as collaborative user interfaces for analysis warnings.

Developing and optimizing software applications for high performance and energy efficiency is a very challenging task, even when considering a single target machine. For instance, optimizing for multicore-based computing systems requires in-depth knowledge about programming languages, application programming interfaces, compilers, performance tuning tools, and computer architecture and organization. Many of the tasks of performance engineering methodologies require manual efforts and the use of different tools not always part of an integrated toolchain. This paper presents Pegasus, a performance engineering approach supported by a framework that consists of a source-to-source compiler, controlled and guided by strategies programmed in a Domain-Specific Language, and an autotuner. Pegasus is a holistic and versatile approach spanning various decision layers composing the software stack, and exploiting the system capabilities and workloads effectively through the use of runtime autotuning. The Pegasus approach helps developers by automating tasks regarding the efficient implementation of software applications in multicore computing systems. These tasks focus on application analysis, profiling, code transformations, and the integration of runtime autotuning. Pegasus allows developers to program their strategies or to automatically apply existing strategies to software applications in order to ensure the compliance of non-functional requirements, such as performance and energy efficiency. We show how to apply Pegasus and demonstrate its applicability and effectiveness in a complex case study, which includes tasks from a smart navigation system.

As static data-flow analysis becomes able to report increasingly complex bugs, using an evergrowing set of complex internal rules encoded into flow functions, the analysis tools themselves grow more and more complex. In result, for users to be able to effectively use those tools on specific codebases, they require special configurations—a task which in industry is typically performed by individual developers or dedicated teams. To efficiently use and configure static analysis tools, developers need to build a certain understanding of the analysis' rules, i.e., how the underlying analyses interpret the analyzed code and their reasoning for reporting certain warnings. In this article, we explore how to assist developers in understanding the analysis' warnings, and finding weaknesses in the analysis' rules. To this end, we introduce the concept of rule graphs that expose to the developer selected information about the internal rules of data-flow analyses. We have implemented rule graphs on top of a taint analysis, and show how the graphs can support the abovementioned tasks. Our user study and empirical evaluation show that using rule graphs helps developers understand analysis warnings more accurately than using simple warning traces, and that rule graphs can help developers identify causes for false positives in analysis rules.

In response to the increasing calls for algorithmic accountability, UML2PROV is a novel approach to address the existing gap between application design, where models are described by UML diagrams, and provenance design, where generated provenance is meant to describe an application’s flows of data, processes and responsibility, enabling greater accountability of this application. The originality of UML2PROV is that designers are allowed to follow their preferred software engineering methodology to create the UML Diagrams for their application, while UML2PROV takes the UML diagrams as a starting point to automatically generate: (1) the design of the provenance to be generated (expressed as PROV templates); and (2) the software library for collecting runtime values of interest (encoded as variable-value associations known as bindings), which can be deployed in the application without developer intervention. At runtime, the PROV templates combined with the bindings are used to generate high-quality provenance suitable for subsequent consumption. UML2PROV is rigorously defined by an extensive set of 17 patterns mapping UML diagrams to provenance templates, and is accompanied by a reference implementation based on Model Driven Development techniques. A systematic evaluation of UML2PROV uses quantitative data and qualitative arguments to show the benefits and trade-offs of applying UML2PROV for software engineers seeking to make applications provenance-aware. In particular, as the UML design drives both the design and capture of provenance, we discuss how the levels of detail in UML designs affect aspects such as provenance design generation, application instrumentation, provenance capability maintenance, storage and run-time overhead, and quality of the generated provenance. Some key lessons are learned such as: starting from a non-tailored UML design leads to the capture of more provenance than required to satisfy provenance requirements and therefore, increases the overhead unnecessarily; alternatively, if the UML design is tailored to focus on addressing provenance requirements, only relevant provenance gets to be collected, resulting in lower overheads.

Mutation testing has been widely used to assess the fault-detection effectiveness of a test suite, as well as to guide test case generation or prioritization. Empirical studies have shown that, while mutants are generally representative of real faults, an effective application of mutation testing requires “traditional” operators designed for programming languages to be augmented with operators specific to an application domain and/or technology. The case for Android apps is not an exception. Therefore, in this paper we describe the process we followed to create (i) a taxonomy of mutation operations and, (ii) two tools, MDroid+ and MutAPK for mutant generation of Android apps. To this end, we systematically devise a taxonomy of 262 types of Android faults grouped in 14 categories by manually analyzing 2,023 software artifacts from different sources (e.g., bug reports, commits). Then, we identified a set of 38 mutation operators, and implemented them in two tools, the first enabling mutant generation at the source code level, and the second designed to perform mutations at APK level. The rationale for having a dual-approach is based on the fact that source code is not always available when conducting mutation testing. Thus, mutation testing for APKs enables new scenarios in which researchers/practitioners only have access to APK files. The taxonomy, proposed operators, and tools have been evaluated in terms of the number of non-compilable, trivial, equivalent, and duplicate mutants generated and their capacity to represent real faults in Android apps as compared to other well-known mutation tools.

Identifying refactoring operations in source code changes is valuable to understand software evolution. Therefore, several tools have been proposed to automatically detect refactorings applied in a system by comparing source code between revisions. The availability of such infrastructure has enabled researchers to study refactoring practice in large scale, leading to important advances on refactoring knowledge. However, although a plethora of programming languages are used in practice, the vast majority of existing studies are restricted to the Java language due to limitations of the underlying tools. This fact poses an important threat to external validity. Thus, to overcome such limitation, in this paper we propose RefDiff 2.0, a multi-language refactoring detection tool. Our approach leverages techniques proposed in our previous work and introduces a novel refactoring detection algorithm that relies on the Code Structure Tree (CST), a simple yet powerful representation of the source code that abstracts away the specificities of particular programming languages. Despite its language-agnostic design, our evaluation shows that RefDiff's precision (96%) and recall (80%) are on par with state-of-the-art refactoring detection approaches specialized in the Java language. Our modular architecture also enables one to seamless extend RefDiff to support other languages via a plugin system. As a proof of this, we implemented plugins to support two other popular programming languages: JavaScript and C. Our evaluation in these languages reveals that precision and recall ranges from 88% to 91%. With these results, we envision RefDiff as a viable alternative for breaking the single-language barrier in refactoring research and in practical applications of refactoring detection.

Context: Multiple types of processing units (e.g., CPUs, GPUs and FPGAs) can be used jointly to achieve better performance in computational systems. However, these units are built with fundamentally different characteristics and demand attention especially towards software deployment. Objective: The goal of this work is to summarize the state-of-the-art of software deployment on heterogeneous platforms. We provide an overview of the research area by searching for and categorizing relevant studies, as well as discussing gaps and trends of the field. We are interested in the main concerns (RQ1) and the approaches used (RQ2) when deploying software on heterogeneous platforms. Method: In order to achieve our goal, we performed a systematic mapping study, which refers to a method for reviewing literature with basis on predefined search strategies and a multi-step selection process. Results: We selected and analyzed 146 primary studies from multiple sources and found that the area of research is dominated by solution proposals. The majority of the studies discuss concerns about scheduling, the quality of the software, and its architecture. A large number of studies focuses on the problem of scheduling tasks and processes. We found approaches that are applied at different binding times (i.e., design time, runtime, orthogonal). Conclusion: The evaluation of the proposed solutions in an industrial context is missing. Also, the proposed methods have not been evaluated in development processes. Most of the methods address a particular concern, or a few concerns, while there is a lack of a holistic approach.

With version 9, Java has been given the new module system Jigsaw. Major goals were to simplify maintainability of the JDK and improve its security by encapsulating modules’ internal types. While the module system successfully limits the visibility of internal types, it does not prevent sensitive data from escaping. Since the module system reasons about types only, objects are allowed to escape even if that module declares the type as internal. Finding such unintended escapes is important, as they may violate a module’s integrity and confidentiality, but is a complex task as it requires one to reason about pointers and type hierarchy. We thus present ModGuard , a novel static analysis based on Doop which complements the Java module system with an analysis to automatically identify instances that escape their declaring module. Along with ModGuard we contribute a complete formal definition of a module’s entrypoints, i.e., the method implementations that a module actually allows other modules to directly invoke. We further make available a novel micro-benchmark suite MIC9Bench to show the effectiveness but also current shortcomings of ModGuard , and to enable comparative studies in the future. Finally, we describe a case study that we conducted using Apache Tomcat, which shows that a migration of applications towards Jigsaw modules does not prevent sensitive instances from escaping, yet also shows that ModGuard is an effective aid in identifying integrity and confidentiality violations of sensitive instances.

The rapid adoption and the diversification of cloud computing technology exacerbate the importance of a sound experimental methodology for this domain. This work investigates how to measure and report performance in the cloud, and how well the cloud research community is already doing it. We propose a set of eight important methodological principles that combine best-practices from nearby fields with concepts applicable only to clouds, and with new ideas about the time-accuracy trade-off. We show how these principles are applicable using a practical use-case experiment. To this end, we analyze the ability of the newly released SPEC Cloud IaaS benchmark to follow the principles, and showcase real-world experimental studies in common cloud environments that meet the principles. Last, we report on a systematic literature review including top conferences and journals in the field, from 2012 to 2017, analyzing if the practice of reporting cloud performance measurements follows the proposed eight principles. Worryingly, this systematic survey and the subsequent two-round human reviews, reveal that few of the published studies follow the eight experimental principles. We conclude that, although these important principles are simple and basic, the cloud community is yet to adopt them broadly to deliver sound measurement of cloud environments.

Microbenchmarking frameworks, such as Java's Microbenchmark Harness (JMH), allow developers to write fine-grained performance test suites at the method or statement level. However, due to the complexities of the Java Virtual Machine, developers often struggle with writing expressive JMH benchmarks which accurately represent the performance of such methods or statements. In this paper, we empirically study bad practices of JMH benchmarks. We present a tool that leverages static analysis to identify 5 bad JMH practices. Our empirical study of 123 open source Java-based systems shows that each of these 5 bad practices are prevalent in open source software. Further, we conduct several experiments to quantify the impact of each bad practice in multiple case studies, and find that bad practices often significantly impact the benchmark results. To validate our experimental results, we constructed seven patches that fix the identified bad practices for six of the studied open source projects, of which six were merged into the main branch of the project. In this paper, we show that developers struggle with accurate Java microbenchmarking, and provide several recommendations to developers of microbenchmarking frameworks on how to improve future versions of their framework.

Assisting and automating software engineering tasks is a state-of-the-art way to support stakeholders of development projects. A common assistance function of IDEs is the auto-completion of source code. Assistance functions, such as auto-completion, are almost entirely missing in modeling tools though auto-completion in general gains continuously more importance in software development. We analyze a user’s performed editing operations in order to anticipate modeling activities and to recommend appropriate auto-completions for them. Editing operations are captured as events and modeling activities are defined as complex event patterns, facilitating the matching by complex-event-processing. The approach provides adapted auto-completions reactively upon each editing operation of the user. We implemented the RapMOD prototype as add-in for the modeling tool Sparx Enterprise Architect™ . A controlled user experiment with 37 participants performing modeling tasks demonstrated the approach’s potential to reduce modeling effort significantly. Users having auto-completions available for a modeling scenario performed the task 27 percent faster, needed to perform 56 percent less actions, and perceived the task 29 percent less difficult.

The semantic versioning ( $\sf{semver}$ semver ) policy is commonly accepted by open source package management systems to inform whether new releases of software packages introduce possibly backward incompatible changes. Maintainers depending on such packages can use this information to avoid or reduce the risk of breaking changes in their own packages by specifying version constraints on their dependencies. Depending on the amount of control a package maintainer desires to have over her package dependencies, these constraints can range from very permissive to very restrictive. This article empirically compares $\sf{ semver}$ semver compliance of four software packaging ecosystems (Cargo, npm, Packagist and Rubygems), and studies how this compliance evolves over time. We explore to what extent ecosystem-specific characteristics or policies influence the degree of compliance. We also propose an evaluation based on the “wisdom of the crowds” principle to help package maintainers decide which type of version constraints they should impose on their dependencies.

Open source software is widely used in the software industry and the academia. Licenses applied to open source software provide the terms for its further use and distribution. Decisions regarding licensing for new software systems are essential for the system's future use. In this paper, we introduce findOSSLicense, a license recommender that guides users into choosing the appropriate open source license for their software under creation. We also introduce our license modeling concept that is used in the recommendation process. The license modeling captures the properties usually found in existing open source licenses following an analysis performed on license texts. The recommendation process of findOSSLicense is based on a hybrid recommender that uses constraint-based, content-based and collaborative filtering giving also space for flexibility in the use of the system by its end-users who can adapt some system properties. User input, but also external sources of information including existing open source projects, are considered for the creation of the recommendations, whereas licenses used in third party software employed in the software are examined on a limited basis. findOSSLicense has been evaluated with the participation of users of various expertise. 1 1

Monolithic systems increasingly suffer from maintainability and scalability issues as they grow in functionality, size, and complexity. It is widely believed that (micro)service-based architectures can alleviate these problems as each service is supposed to have the following characteristics: clearly defined functionality , sufficient modularity , and the ability to evolve independently . Industrial practices show that service extraction from a legacy monolithic system is labor-intensive and complex. Existing work on service candidate identification aims to group entities of a monolithic system into potential service candidates, but this process has two major challenges: first, it is difficult to extract service candidates with consistent quality; second, it is hard to evaluate the identified service candidates regarding the above three characteristics. To address these challenges, this paper proposes the Functionality-oriented Service Candidate Identification ( FoSCI ) framework to identify service candidates from a monolithic system. Our approach is to record the monolith's execution traces, and extract services candidates using a search-based functional atom grouping algorithm. We also contribute a comprehensive service candidate evaluation suite that uses interface information, structural/conceptual dependency, and commit history. This evaluation system consists of 8 metrics, measuring functionality, modularity, and evolvability respectively of identified service candidates. We compare FoSCI with three existing methods, using 6 widely-used open-source projects as our evaluation subjects. Our results show that FoSCI outperforms existing methods in most measures.

In large-scale software systems, error-prone or change-prone files rarely stand alone. They are typically architecturally connected and their connections usually exhibit architecture problems causing the propagation of error-proneness or change-proneness. In this paper, we propose and empirically validate a suite of architecture anti-patterns that occur in all large-scale software systems and are involved in high maintenance costs. We define these architecture anti-patterns based on fundamental design principles and Baldwin and Clark’s design rule theory. We can automatically detect these anti-patterns by analyzing a project’s structural relationships and revision history. Through our analyses of 19 large-scale software projects, we demonstrate that these architecture anti-patterns have significant impact on files’ bug-proneness and change-proneness. In particular, we show that 1) files involved in these architecture anti-patterns are more error-prone and change-prone; 2) the more anti-patterns a file is involved in, the more error-prone and change-prone it is; and 3) while all of our defined architecture anti-patterns contribute to file’s error-proneness and change-proneness, Unstable Interface and Crossing contribute the most by far.

Productivity of a software development organization can be enhanced by improving the software process, using better tools/technology, and enhancing the productivity of programmers. This work focuses on improving programmer productivity by studying the process used by a programmer for executing an assigned task, which we call the task process. We propose a general framework for studying the impact of task processes on programmer productivity and also the impact of transferring task processes of high-productivity programmers to average-productivity peers. We applied the framework to a few live projects in Robert Bosch Engineering and Business Solutions Limited, a CMMI Level 5 company. In each project, we identified two groups of programmers: high-productivity and average-productivity programmers. We requested each programmer to video capture their computer screen while executing his/her assigned tasks. We then analyzed these task videos to extract the task processes and then used them to identify the differences between the task processes used by the two groups. Some key differences were found between the task processes, which could account for the difference in productivities of the two groups. Similarities between the task processes were also analyzed quantitatively by modeling each task process as a Markov chain. We found that programmers from the same group used similar task processes, but the task processes of the two groups differed considerably. The task processes of high-productivity programmers were transferred to the average-productivity programmers by training them on the key steps missing in their process but commonly present in the work of their high-productivity peers. A substantial productivity gain was found in the average-productivity programmers as a result of this transfer. The study shows that task processes of programmers impact their productivity, and it is possible to improve the productivity of average-productivity programmers by transferring task processes from high-productivity programmers to them.

Nearly all facets of our everyday life strongly depend on software-intensive systems. Besides correctness, highly relevant quality properties of these systems include performance, as directly perceived by the user, and maintainability, as an important decision factor for evolution. These quality properties strongly depend on architectural design decisions. Hence, to ensure high quality, research and practice is interested in approaches to analyze the system architecture for quality properties. Therefore, models of the system architecture are created and used for analysis. Many different languages (often defined by metamodels) exist to model the systems and reason on their quality. Such languages are mostly specific to quality properties, tools or development paradigms. Unfortunately, the creation of a specific model for any quality property of interest and any different tool used is simply infeasible. Current metamodels for quality modeling and analysis are often not designed to be extensible and reusable. Experience from generalizing and extending metamodels result in hard to evolve and overly complex metamodels. A systematic way of creating, extending and reusing metamodels for quality modeling and analysis, or parts of them, does not exist yet. When comparing metamodels for different quality properties, however, substantial parts show quite similar language features. This leads to our approach to define the first reference architecture for metamodels for quality modeling and analysis. A reference architecture in software engineering provides a general architecture for a given application domain. In this paper, we investigate the applicability of modularization concepts from object-oriented design and the idea of a reference architecture to metamodels for quality modeling and analysis to systematically create, extend and reuse metamodel parts. Thus, the reference architecture allows to tailor metamodels. Requirements on the reference architecture are gathered from a historically grown metamodel. We specify modularization concepts as a foundation of the reference architecture. Detailed application guidelines are described. We argue the reference architecture supports instance compatibility and non-intrusive, independent extension of metamodels. In four case studies, we refactor historically grown metamodels and compare them to the original metamodels. The study results show the reference architecture significantly improves evolvability as well as need-specific use and reuse of metamodels.

Application programming interfaces (APIs) continually evolve to meet ever-changing user needs, and documentation provides an authoritative reference for their usage. However, API documentation is commonly outdated because nearly all of the associated updates are performed manually. Such outdated documentation, especially with regard to API names, causes major software development issues. In this paper, we propose a method for automatically updating outdated API names in API documentation. Our insight is that API updates in documentation can be derived from API implementation changes between code revisions. To evaluate the proposed method, we applied it to four open source projects. Our evaluation results show that our method, FreshDoc, detects outdated API names in API documentation with 48 percent higher accuracy than the existing state-of-the-art methods do. Moreover, when we checked the updates suggested by FreshDoc against the developers’ manual updates in the revised documentation, FreshDoc detected 82 percent of the outdated names. When we reported 40 outdated API names found by FreshDoc via issue tracking systems, developers accepted 75 percent of the suggestions. These evaluation results indicate that FreshDoc can be used as a practical method for the detection and updating of API names in the associated documentation.

Software engineering is now more than ever a community effort. Its success often weighs on balancing distance, culture, global engineering practices and more. In this scenario many unforeseen socio-technical events may result into additional project cost or ?social" debt, e.g., sudden, collective employee turnover. With industrial research we discovered community smells, that is, sub-optimal patterns across the organisational and social structure in a software development community that are precursors of such nasty socio-technical events. To understand the impact of community smells at large, in this paper we first introduce CodeFace4Smells, an automated approach able to identify four community smell types that reflect socio-technical issues that have been shown to be detrimental both the software engineering and organisational research fields. Then, we perform a large-scale empirical study involving over 100 years worth of releases and communication structures data of 60 open-source communities: we evaluate (i) their diffuseness, i.e., how much are they distributed in open-source, (ii) how developers perceive them, to understand whether practitioners recognize their presence and their negative effects in practice, and (iii) how community smells relate to existing socio-technical factors, with the aim of assessing the inter-relations between them. The key findings of our study highlight that community smells are highly diffused in open-source and are perceived by developers as relevant problems for the evolution of software communities. Moreover, a number of state-of-the-art socio-technical indicators (e.g., socio-technical congruence) can be used to monitor how healthy a community is and possibly avoid the emergence of social debt.

In this paper, we study the app store as a phenomenon from the developers’ perspective to investigate the extent to which app stores affect software engineering tasks. Through developer interviews and questionnaires, we uncover findings that highlight and quantify the effects of three high-level app store themes: bridging the gap between developers and users, increasing market transparency and affecting mobile release management. Our findings have implications for testing, requirements engineering and mining software repositories research fields. These findings can help guide future research in supporting mobile app developers through a deeper understanding of the app store-developer interaction.

The complexity and dynamism of microservice systems pose unique challenges to a variety of software engineering tasks such as fault analysis and debugging. In spite of the prevalence and importance of microservices in industry, there is limited research on the fault analysis and debugging of microservice systems. To fill this gap, we conduct an industrial survey to learn typical faults of microservice systems, current practice of debugging, and the challenges faced by developers in practice. We then develop a medium-size benchmark microservice system (being the largest and most complex open source microservice system within our knowledge) and replicate 22 industrial fault cases on it. Based on the benchmark system and the replicated fault cases, we conduct an empirical study to investigate the effectiveness of existing industrial debugging practices and whether they can be further improved by introducing the state-of-the-art tracing and visualization techniques for distributed systems. The results show that the current industrial practices of microservice debugging can be improved by employing proper tracing and visualization techniques and strategies. Our findings also suggest that there is a strong need for more intelligent trace analysis and visualization, e.g., by combining trace visualization and improved fault localization, and employing data-driven and learning-based recommendation for guided visual exploration and comparison of traces.

Application Programming Interfaces (APIs) represent key tools for software developers to build complex software systems. However, several studies have revealed that even major API providers tend to have incomplete or inconsistent API documentation. This can severely hamper the API comprehension and, as a consequence, the quality of the software built on them. In this paper, we propose DRONE (Detect and Repair of dOcumentatioN dEfects), a framework to automatically detect and repair defects from API documents by leveraging techniques from program analysis, natural language processing, and constraint solving. Specifically, we target at the directives of API documents, which are related to parameter constraints and exception handling declarations. Furthermore, in presence of defects, we also provide a prototypical repair recommendation system. We evaluate our approach on parts of the well-documented APIs of JDK 1.8 APIs (including javaFX) and Android 7.0 (level 24). Across the two empirical studies, our approach can detect API defects with an average F-measure of 79.9, 71.7, and 81.4 percent, respectively. The API repairing capability has also been evaluated on the generated recommendations in a further experiment. User judgments indicate that the constraint information is addressed correctly and concisely in the rendered directives.

Refactoring improves he software design while preserving overall functionality and behavior, and is an important technique in managing the growing complexity of software systems. This disclosure introduces an interactive way to refactor software systems using innovization and interactive dynamic multi-objective optimization. The interactive approach supports the adaption of refactoring solutions based on developer feedback while also taking into account other code changes that the developer may have performed in parallel with the refactoring activity.

To detect and fix bugs and security vulnerabilities, software companies use static analysis as part of the development process. However, static analysis code itself is also prone to bugs. To ensure a consistent level of precision, as analyzed programs grow more complex, a static analysis has to handle more code constructs, frameworks, and libraries that the programs use. While more complex analyses are written and used in production systems every day, the cost of debugging and fixing them also increases tremendously. To better understand the difficulties of debugging static analyses, we surveyed 115 static analysis writers. From their responses, we extracted the core requirements to build a debugger for static analysis, which revolve around two main issues: (1) abstracting from two code bases at the same time (the analysis code and the analyzed code) and (2) tracking the analysis internal state throughout both code bases. Most current debugging tools that our survey participants use lack the capabilities to address both issues. Focusing on those requirements, we introduce VisuFlow, a debugging environment for static data-flow analysis that is integrated in the Eclipse development environment. VisuFlow features graph visualizations that enable users to view the state of a data-flow analysis and its intermediate results at any time. Special breakpoints in VisuFlow help users step through the analysis code and the analyzed simultaneously. To evaluate the usefulness of VisuFlow, we have conducted a user study on 20 static analysis writers. Using VisuFlow helped our sample of analysis writers identify 25% and fix 50% more errors in the analysis code compared to using the standard Eclipse debugging environment.