An empirical characterization of software bugs in open-source Cyber–Physical Systems

Zampetti, Fiorella; Kapur, Ritu; Penta, Massimiliano Di; Panichella, Sebastiano

doi:10.1016/j.jss.2022.111425

Cited by 24 publications

(8 citation statements)

References 43 publications

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“…To answer these two research questions, we analyzed the code history of 14 open-source CPS projects publicly available on GitHub and used in prior studies related to CPSs (Zampetti et al, 2022). We examined commits that reported and fixed self-admitted performance issues by analyzing (1) commit messages, (2) code and project documentation, and (3) code changes.…”

Section: Rq2: How Prevalent Are Cps-specific Performance Antipatterns...mentioning

confidence: 99%

“…• Programming language: the project must be written in C, C++, Java, JavaScript, or Python. While C, C++ are the most common programming languages for CPS (Zampetti et al, 2022), we also consider other programming languages to increase the generalizability of our results. Therefore, the resulting selection of projects must be a diverse selection of programming languages used.…”

Section: Subjectsmentioning

confidence: 99%

“…This resulted in 14 projects, which are described in Table 2. Notice that the projects written in C, C++, and Python have also been used in a prior study (Zampetti et al, 2022) aimed at characterizing CPSs according to the type of functional bugs/issues they contain. Our selection comprises projects with various maturity levels: PX4-Autopilot and Vacuum Robot Mark II have the highest and lowest contributions, with 35,537 and 54 commits to the main branch, respectively.…”

Section: Subjectsmentioning

confidence: 99%

See 2 more Smart Citations

The Slow and the Furious? Performance Antipattern Detection in Cyber-Physical Systems

Dinten

Derakhshanfar

Panichella

et al. 2023

Preprint

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Section: Rq2: How Prevalent Are Cps-specific Performance Antipatterns...mentioning

confidence: 99%

Section: Subjectsmentioning

confidence: 99%

Section: Subjectsmentioning

confidence: 99%

See 1 more Smart Citation

The Slow and the Furious? Performance Antipattern Detection in Cyber-Physical Systems

Dinten

Derakhshanfar

Panichella

et al. 2023

Preprint

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“…Building openly available corpora using the appropriate configuration for the generators, with a description of the characteristics applicable evaluations, would avoid such issues. More in general, having the JUGE infrastructure and its associated standards available to industrial and academic research communities can open the road for more systematic testing for other languages (e.g., Python) as well as the definition of testing pipelines to identify bugs and imperfections of systems in other application domains [86][87][88][89]. The availability of such technologies can also impact computer science education, with available tools that can be used in practical courses.…”

Section: Future Applicationsmentioning

confidence: 99%

JUGE: An infrastructure for benchmarking Java unit test generators

Devroey

Gambi

Galeotti

et al. 2022

Software Testing Verif & Rel

Self Cite

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Summary 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 various platforms (e.g., desktop, web, or mobile applications). The generators exhibit varying effectiveness and efficiency, depending on the testing goals they aim to satisfy (e.g., unit‐testing of libraries versus 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 by systematically executing large‐scale evaluations of different generators. However, executing such empirical evaluations is not trivial and requires substantial effort to select appropriate benchmarks, setup the evaluation infrastructure, and collect and analyse the results. In this Software Note, we present our JUnit Generation Benchmarking Infrastructure (JUGE) supporting generators (search‐based, random‐based, symbolic execution, etc.) seeking to automate the production of unit tests for various purposes (validation, regression testing, fault localization, etc.). The primary goal is to reduce the overall benchmarking 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, several editions of a unit testing tool competition, co‐located with the Search‐Based Software Testing Workshop, have taken place where JUGE was used and evolved. As a result, an increasing amount of tools (over 10) from academia and industry have been evaluated on JUGE, matured over the years, and allowed the identification of future research directions. Based on the experience gained from the competitions, we discuss the expected impact of JUGE in improving the knowledge transfer on tools and approaches for test generation between academia and industry. Indeed, the JUGE infrastructure demonstrated an implementation design that is flexible enough to enable the integration of additional unit test generation tools, which is practical for developers and allows researchers to experiment with new and advanced unit testing tools and approaches.

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“…To deal with such safety-related challenges, there is an increasing interest in adopting agile development paradigms within the CPS safety-critical domains [6,7] to identify hazards and elicit safety requirements iteratively [8]. Consequently, researchers proposed the usage of Digital-Twins 1 technologies to simulate and test CPSs in a diversified set of scenarios [9,10,11,12,13] to support testing automation [14,15], regression testing [12,16], and debugging [17,18] activities.…”

Section: Introductionmentioning

confidence: 99%

Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor

Birchler

Ganz

Khatiri

et al. 2022

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

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Simulation platforms facilitate the continuous development of complex systems such as self-driving cars (SDCs). However, previous results on testing SDCs using simulations have shown that most of the automatically generated tests do not strongly contribute to establishing confidence in the quality and reliability of the SDC. Therefore, those tests can be characterized as "uninformative", and running them generally means wasting precious computational resources. We address this issue with SDC-Scissor, a framework that leverages Machine Learning to identify simulation-based tests that are unlikely to detect faults in the SDC software under test and skip them before their execution. Consequently, by filtering out those tests, SDC-Scissor reduces the number of long-running simulations to execute and drastically increases the cost-effectiveness of simulation-based testing of SDCs software. Our evaluation concerning two large datasets and around 12'000 tests showed that SDC-Scissor achieved a higher classification F1-score (between 47% and 90%) than a randomized baseline in identifying tests that lead to a fault and reduced the time spent running uninformative tests (speedup between 107% and 170%).

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An empirical characterization of software bugs in open-source Cyber–Physical Systems

Cited by 24 publications

References 43 publications

The Slow and the Furious? Performance Antipattern Detection in Cyber-Physical Systems

The Slow and the Furious? Performance Antipattern Detection in Cyber-Physical Systems

JUGE: An infrastructure for benchmarking Java unit test generators

Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor

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