Auflistung P332 - Software Engineering 2023 nach Erscheinungsdatum
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- KonferenzbeitragEvaluation of Usability Criteria Addressed by Static Analysis Tools on a Large Scale(Software Engineering 2023, 2023) Nachtigall, Marcus; Schlichtig, Michael; Bodden, EricStatic analysis tools support developers in detecting potential coding issues, such as bugs or vulnerabilities. Research emphasizes technical challenges of such tools but also mentions severe usability shortcomings. These shortcomings hinder the adoption of static analysis tools, and user dissatisfaction may even lead to tool abandonment. To comprehensively assess the state of the art, we present the first systematic usability evaluation of a wide range of static analysis tools. We derived a set of 36 relevant criteria from the literature and used them to evaluate a total of 46 static analysis tools complying with our inclusion and exclusion criteria - a representative set of mainly non-proprietary tools. The evaluation against the usability criteria in a multiple-raters approach shows that two thirds of the considered tools off er poor warning messages, while about three-quarters provide hardly any fix support. Furthermore, the integration of user knowledge is strongly neglected, which could be used for instance, to improve handling of false positives. Finally, issues regarding workflow integration and specialized user interfaces are revealed. These findings should prove useful in guiding and focusing further research and development in user experience for static code analyses.
- KonferenzbeitragOn the validity of pre-trained transformers for natural language processing in the software engineering domain(Software Engineering 2023, 2023) von der Mosel, Julian; Trautsch, Alexander; Herbold, SteffenWe summarize the article On the validity of pre-trained transformers for natural language processing in the software engineering domain [VTH22], which was published in the IEEE Transactions on Software Engineering in 2022.
- KonferenzbeitragProblems with with SZZ and Features: An empirical assessment of the state of practice of defect prediction data collection(Software Engineering 2023, 2023) Herbold, Steffen; Trautsch, Alexander; Trautsch, Fabian; Ledel, BenjaminWe summarize the article Problems with with SZZ and Features: An empirical assessment of the state of practice of defect prediction data collection [He22], which was published in Empirical Software Engineering in 2022.
- KonferenzbeitragProperty-Driven Black-Box Testing of Numeric Functions(Software Engineering 2023, 2023) Sharma, Arnab; Melnikov, Vitalik; Hüllermeier, Eyke; Wehrheim, HeikeIn this work, we propose a property-driven testing mechanism to perform unit testing of functions performing numerical computations. Our approach, similar to the property-based testing technique, allows the tester to specify the requirements to check. Unlike property-based testing, the specification is then used to generate test cases in a targeted manner. Moreover, our approach works as a black-box testing tool, i.e. it does not require knowledge about the internals of the function under test. Therefore, besides on programmed numeric functions, we also apply our technique to machine-learned regression models. The experimental evaluation on a number of case studies shows the effectiveness of our testing approach.
- KonferenzbeitragA comprehensive empirical evaluation of generating test suites for mobile applications with diversity - Summary(Software Engineering 2023, 2023) Vogel, Thomas; Tran, Chinh; Grunske, LarsIn this extended abstract, we summarize our work on analyzing the fitness landscape of the search-based app testing problem and building on that, improving and evaluating a specific solution for this problem. This work has been published under the title of “A comprehensive empirical evaluation of generating test suites for mobile applications with diversity” in the journal Information and Software Technology (IST) in 2021 [VTG21].
- KonferenzbeitragIncremental Software Product Line Verification - A Performance Analysis with Dead Variable Code(Software Engineering 2023, 2023) Kröher, Christian; Flöter, Moritz; Gerling, Lea; Schmid, KlausIn this work, we summarize our journal paper published in Empirical Software Engineering (EMSE) in 2022 [Kr22]. Verification approaches for Software Product Lines (SPL) aim at detecting variability-related defects and inconsistencies. In general, these analyses take a significant amount of time to provide complete results for an entire, complex SPL. If the SPL evolves, these results potentially become invalid, which requires a time-consuming re-verification of the entire SPL for each increment. However, in previous work we showed that variability-related changes occur rather infrequently and typically only affect small parts of a SPL. In this paper, we utilize this observation and present an incremental dead variable code analysis as an example for incremental SPL verification, which achieves significant performance improvements. It explicitly considers changes and partially updates its previous results by re-verifying changed artifacts only. We apply this approach to the Linux kernel demonstrating that our fastest incremental strategy takes only 3.20 seconds or less for most of the changes, while the non-incremental approach takes 1,020 seconds in median. We also discuss the impact of different variants of our strategy on the overall performance, providing insights into optimizations that are worthwhile.
- KonferenzbeitragJicer: Slicing Android Apps for Cooperative Analysis(Software Engineering 2023, 2023) Pauck, Felix; Wehrheim, HeikeSlicing allows to identify which program parts influence or are influenced by a certain statement of a program. Hence, if we know which statement is potentially causing an issue we can slice accordingly to only inspect the slice while debugging. With Jicer, we proposed a slicer that can be used in a different context, namely cooperative Android app analysis. In combination with taint analysis tools, we employed Jicer to get more accurate results.
- KonferenzbeitragA Summary of ReVision: History-based Model Repair Recommendations(Software Engineering 2023, 2023) Ohrndorf, Manuel; Pietsch, Christopher; Kelter, Udo; Grunske, Lars; Kehrer, TimoThis work reports recent research results on history-based model repair recommendations in Model-Driven Engineering (MDE), originally published in Reference [Oh21]. Models in MDE are primary development artifacts that are heavily edited in all software development stages and can become temporarily inconsistent during editing. Model repair tools can support developers by proposing a list of the most promising repairs. Such repair recommendations will only be accepted in practice if the generated proposals are plausible and understandable and the set as a whole is manageable. Our interactive repair tool ReVision [Oh18], aims at generating repair proposals for inconsistencies introduced by past incomplete edit steps. Such an incomplete edit step is either undone or extended to the full execution of a consistency-preserving edit operation. We evaluate our approach using histories of real-world models from popular open-source modeling projects. Our experimental results confirm our hypothesis that most of the inconsistencies can be resolved by complementing incomplete edits. In fact, 92.2% of the proposed complementations could be observed in the model history.
- KonferenzbeitragPeer-Reviewing and Submission Dynamics Around Top Software-Engineering Venues: A Juniors’ Perspective(Software Engineering 2023, 2023) Alchokr, Rand; Krüger, Jacob; Shakeel, Yusra; Saake, Gunter; Leich, ThomasResearch is an intrinsically challenging process full of obstacles. However, these obstacles may be more dominant for a specific group of researchers (such as junior researchers) compared to others. It is the responsibility of the community to pay close attention to those groups that may be struggling for unfair reasons and provide necessary support. Junior researchers are of high importance to the scientific community, and are defined as young researchers who have recently started their research career[ Li19]. Despite their importance, juniors may face impediments when starting their career that hinder their activities and motivation. For instance, collaboration aspects and peer-reviewing models can play a role. Junior researchers without a high reputation (e.g., via their co-authors) may be negatively impacted by reputation biases, and thus could have even more problems with publishing and building their reputation independently. In our study, we investigate what challenges junior researchers perceive when submitting their work to software-engineering venues with a high reputation.
- KonferenzbeitragIt’s Your Loss: Classifying Information Loss During Variability Model Roundtrip Transformations(Software Engineering 2023, 2023) Feichtinger, Kevin; Sundermann, Chico; Thüm, Thomas; Rabiser, RickThis is a summary of a paper (with the same title) originally published at the 26th ACM International Systems and Software Product Line Conference (SPLC) in 2022 discussing the information loss occurring when transforming variability models.