JUGE: An Infrastructure for Benchmarking Java Unit Test Generators

Researchers and practitioners have designed and implemented various automated test case generators to support effective software testing. Such generators exist for various languages (e.g., Java, C#, or Python) and for various platforms (e.g., desktop, web, or mobile applications). Such generators exhibit varying effectiveness and efficiency, depending on the testing goals they aim to satisfy (e.g., unit-testing of libraries vs. system-testing of entire applications) and the underlying techniques they implement. In this context, practitioners need to be able to compare different generators to identify the most suited one for their requirements, while researchers seek to identify future research directions. This can be achieved through the systematic execution of large-scale evaluations of different generators. However, the execution of such empirical evaluations is not trivial and requires a substantial effort to collect benchmarks, setup the evaluation infrastructure, and collect and analyse the results. In this paper, we present our JUnit Generation benchmarking infrastructure (JUGE) supporting generators (e.g., search-based, random-based, symbolic execution, etc.) seeking to automate the production of unit tests for various purposes (e.g., validation, regression testing, fault localization, etc.). The primary goal is to reduce the overall effort, ease the comparison of several generators, and enhance the knowledge transfer between academia and industry by standardizing the evaluation and comparison process. Since 2013, eight editions of a unit testing tool competition, co-located with the Search-Based Software Testing Workshop, have taken place and used and updated JUGE. As a result, an increasing amount of tools (over ten) from both academia and industry have been evaluated on JUGE, matured over the years, and allowed the identification of future research directions

SBST tool competition 2021

​© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We report on the organization, challenges, and results of the ninth edition of the Java Unit Testing Competition as well as the first edition of the Cyber-Physical Systems Testing Tool Competition. Java Unit Testing Competition. This year, five tools, Randoop, UtBot, Kex, Evosuite, and EvosuiteDSE, were executed on a benchmark with (i) new classes under test, selected from three open-source software projects, and (ii) the set of classes from three projects considered in the eighth edition. We relied on an improved Docker infrastructure to execute the tools and the subsequent coverage and mutation analysis. Given the high number of participants, we considered only two time budgets for test case generation: thirty seconds and two minutes. Cyber-Physical Systems Testing Tool Competition. Five tools, Deeper, Frenetic, GABExplore, GABExploit, and Swat, competed on testing self-driving car software by generating simulationbased tests using our new testing infrastructure. We considered two experimental settings to study test generators’ transitory and asymptotic behaviors and evaluated the tools’ test generation effectiveness and the exposed failures’ diversity. This paper describes our methodology, the statistical analysis of the results together with the contestant tools, and the challenges faced while running the competition experiments

Software Test Case Generation Tools and Techniques: A Review

Software Industry is evolving at a very fast pace since last two decades. Many software developments, testing and test case generation approaches have evolved in last two decades to deliver quality products and services. Testing plays a vital role to ensure the quality and reliability of software products. In this paper authors attempted to conduct a systematic study of testing tools and techniques. Six most popular e-resources called IEEE, Springer, Association for Computing Machinery (ACM), Elsevier, Wiley and Google Scholar to download 738 manuscripts out of which 125 were selected to conduct the study. Out of 125 manuscripts selected, a good number approx. 79% are from reputed journals and around 21% are from good conference of repute. Testing tools discussed in this paper have broadly been divided into five different categories: open source, academic and research, commercial, academic and open source, and commercial & open source. The paper also discusses several benchmarked datasets viz. Evosuite 10, SF100 Corpus, Defects4J repository, Neo4j, JSON, Mocha JS, and Node JS to name a few. Aim of this paper is to make the researchers aware of the various test case generation tools and techniques introduced in the last 11 years with their salient features

An empirical investigation of relevant changes and automation needs in modern code review

SUMMARY of the PAPER: This paper investigates the approaches and tools that, from a "developer's point of view", are still needed to facilitate Modern Code Review (MCR) activities. To that end, we empirically elicited a taxonomy of recurrent review change types that characterize MCR. This by (i) qualitatively and quantitatively analyzing review changes/commits of ten open-source projects; (ii) integrating MCR change types from existing taxonomies available from the literature; and (iii) surveying 52 developers to integrate eventually missing change types in the taxonomy. The results of our study highlight that the availability of new emerging development technologies (e.g., cloud-based technologies) and practices (e.g., continuous delivery) has pushed developers to perform additional activities during MCR and that additional types of feedback are expected by reviewers. Our participants provided also recommendations, specified techniques to employ, and highlighted the data to analyze for building recommender systems able to automate the code review activities composing our taxonomy. In summary, this study sheds some more light on the approaches and tools that are still needed to facilitate MCR activities, confirming the feasibility and usefulness of using summarization techniques during MCR activities. We believe that the results of our work represent an essential step for meeting the expectations of developers and supporting the vision of full or partial automation in MCR. REPLICATION PACKAGE: https://zenodo.org/record/3679402#.XxgSgy17Hxg PREPRINT: https://spanichella.github.io/img/EMSE-MCR-2020.pdfRecent research has shown that available tools for Modern Code Review (MCR) are still far from meeting the current expectations of developers. The objective of this paper is to investigate the approaches and tools that, from a developer's point of view, are still needed to facilitate MCR activities. To that end, we first empirically elicited a taxonomy of recurrent review change types that characterize MCR. The taxonomy was designed by performing three steps: (i) we generated an initial version of the taxonomy by qualitatively and quantitatively analyzing 211 review changes/commits and 648 review comments of ten open-source projects; then (ii) we integrated into this initial taxonomy, topics, and MCR change types of an existing taxonomy available from the literature; finally, (iii) we surveyed 52 developers to integrate eventually missing change types in the taxonomy. Results of our study highlight that the availability of new emerging development technologies (e.g., cloud-based technologies) and practices (e.g., continuous delivery) has pushed developers to perform additional activities during MCR and that additional types of feedback are expected by reviewers. Our participants provided recommendations, specified techniques to employ, and highlighted the data to analyze for building recommender systems able to automate the code review activities composing our taxonomy. We surveyed 14 additional participants (12 developers and 2 researchers), not involved in the previous survey, to qualitatively assess the relevance and completeness of the identified MCR change types as well as assess how critical and feasible to implement are some of the identified techniques to support MCR activities. Thus, with a study involving 21 additional developers, we qualitatively assess the feasibility and usefulness of leveraging natural language feedback (automation considered critical/feasible to implement) in supporting developers during MCR activities. In summary, this study sheds some more light on the approaches and tools that are still needed to facilitate MCR activities, confirming the feasibility and usefulness of using summarization techniques during MCR activities. We believe that the results of our work represent an essential step for meeting the expectations of developers and supporting the vision of full or partial automation in MC

Using Genetic Programming to Build Self-Adaptivity into Software-Defined Networks

Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that, rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. While the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, e.g., to increase performance and to make a running system more robust. We instantiate and empirically examine the above idea for software-defined networking -- a key enabling technology for modern data centres and Internet of Things applications. Using genetic programming,(GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open-source synthetic and industrial data, indicates that, compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, reduces the frequency of adaptation interventions over time. In addition, we show that, for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss.Comment: arXiv admin note: text overlap with arXiv:2205.0435

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 24th International Conference on Fundamental Approaches to Software Engineering, FASE 2021, which took place during March 27–April 1, 2021, and was held as part of the Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg but changed to an online format due to the COVID-19 pandemic. The 16 full papers presented in this volume were carefully reviewed and selected from 52 submissions. The book also contains 4 Test-Comp contributions

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering, FASE 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 23 full papers, 1 tool paper and 6 testing competition papers presented in this volume were carefully reviewed and selected from 81 submissions. The papers cover topics such as requirements engineering, software architectures, specification, software quality, validation, verification of functional and non-functional properties, model-driven development and model transformation, software processes, security and software evolution

CODE-CHANGE AWARE MUTATION BASED TESTING IN CONTINUOUSLY EVOLVING SYSTEMS

In modern software development practices, testing activities must be carried out frequently and preferably after each code change to bring confidence in anticipated system behaviour and, more importantly, to avoid introducing faults. When it comes to software testing, it is not only about what we are expecting; it is equally about what we are not expecting. Developers desire to test and assess the testing adequacy of the delta of behaviours between stable and modified software versions. Many test adequacy criteria have been proposed through the years, yet very few have been placed for continuous development. Among all proposed, one has been empirically verified to be the most effective in finding faults and evaluating test adequacy. Mutation Testing has been widely studied, but its current traditional form is impractical to keep up with the rapid pace of modern software development standards and code evolution due to a large number of test requirements, i.e., mutants. This dissertation proposes change-aware mutation testing, a novel approach that points to relevant change-aware test requirements, allows reasoning to what extent code modification is tested and captures behavioural relations of changed and unchanged code from which faults often arise. In particular, this dissertation builds contributions around challenges related to the code-mutants' behavioural properties, testing regular code modifications and mutants' fault detection effectiveness. First, this dissertation examines the ability of the mutants to capture the behaviour of regression faults and evaluates the relationship between the syntactic and semantic distance metrics often used to capture mutant-real fault similarity. Second, this dissertation proposes a commit-aware mutation testing approach that focuses rather on change-aware mutants that bring significant values in capturing regression faults. The approach shows 30\% higher fault detection in comparison with baselines and sheds light on the suitability of commit-aware mutation testing in the context of evolving systems. Third, this dissertation proposes the usage of high-order mutations to identify change-impacted mutants, resulting in the most extensive dataset, to date, of commit-relevant mutants, which are further thoroughly studied to provide the understanding and elicit properties of this particular novel category. The studies led to the discovery of long-standing mutants, demonstrated as suitable to maintain a high-quality test suite for a series of code releases. Fourth, this dissertation proposes the usage of learning-based mutant selection strategies when questioning how effective are the mutants of fundamentally different mutation generation approaches in finding faults. The outcomes raise awareness of the risk that the suitability of different kinds of mutants can be misinterpreted if not using intelligent approaches to remove the noise of impractical mutants. Overall, this dissertation proposes a novel change-aware testing approach and provides insights for software testing gatekeepers towards more effective mutation testing in the context of continuously evolving systems