Are Multi-Language Design Smells Fault-Prone? An Empirical Study

Abidi, Mouna; Rahman, Saidur; Openja, Moses; Khomh, Foutse

doi:10.1145/3432690

Cited by 26 publications

(9 citation statements)

References 55 publications

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“…They found that implementing a project with more PLs would increase its defect proneness. Abidi et al analyzed the source code of the MPL system, and found six anti-patterns [22] and twelve code smells [23]. Li et al analyzed 18 Apache MPL software projects, and confirmed that the use of multiple PLs is related to the increase of development difficulty and the decline of software quality [24].…”

Section: B Impact Of the Use Of Multiple Pls On Software Systemsmentioning

confidence: 99%

Understanding Bugs in Multi-Language Deep Learning Frameworks

Li¹,

Wang²,

Wang³

et al. 2023

Preprint

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Deep learning frameworks (DLFs) have been playing an increasingly important role in this intelligence age since they act as a basic infrastructure for an increasingly wide range of AIbased applications. Meanwhile, as multi-programming-language (MPL) software systems, DLFs are inevitably suffering from bugs caused by the use of multiple programming languages (PLs). Hence, it is of paramount significance to understand the bugs (especially the bugs involving multiple PLs, i.e., MPL bugs) of DLFs, which can provide a foundation for preventing, detecting, and resolving bugs in the development of DLFs. To this end, we manually analyzed 1497 bugs in three MPL DLFs, namely MXNet, PyTorch, and TensorFlow. First, we classified bugs in these DLFs into 12 types (e.g., algorithm design bugs and memory bugs) according to their bug labels and characteristics. Second, we further explored the impacts of different bug types on the development of DLFs, and found that deployment bugs and memory bugs negatively impact the development of DLFs in different aspects the most. Third, we found that 28.6%, 31.4%, and 16.0% of bugs in MXNet, PyTorch, and TensorFlow are MPL bugs, respectively; the PL combination of Python and C/C++ is most used in fixing more than 92% MPL bugs in all DLFs. Finally, the code change complexity of MPL bug fixes is significantly greater than that of single-programming-language (SPL) bug fixes in all the three DLFs, while in PyTorch MPL bug fixes have longer open time and greater communication complexity than SPL bug fixes. These results provide insights for bug management in DLFs.

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Section: B Impact Of the Use Of Multiple Pls On Software Systemsmentioning

confidence: 99%

Understanding Bugs in Multi-Language Deep Learning Frameworks

Li¹,

Wang²,

Wang³

et al. 2023

Preprint

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“…To extract bug-fix commits from the collected repositories, we searched commits' messages for a list of bug-related keywords (bug, fail, crash, fix, resolve, failure, broken, break, error, hang, problem, overflow, issue, stop, etc. ), which are used successfully in the literature [3,2,4]. We also used PyDriller [65], a python library to mine the GitHub repositories, to collect bug-fix commits.…”

Section: Selection Of Bug-fix Commitsmentioning

confidence: 99%

Bugs in Machine Learning-based Systems: A Faultload Benchmark

Morovati¹,

Nikanjam²,

Khomh³

et al. 2022

Preprint

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The rapid escalation of applying Machine Learning (ML) in various domains has led to paying more attention to the quality of ML components. There is then a growth of techniques and tools aiming at improving the quality of ML components and integrating them into the ML-based system safely. Although most of these tools use bugs' lifecycle, there is no standard benchmark of bugs to assess their performance, compare them and discuss their advantages and weaknesses. In this study, we firstly investigate the reproducibility and verifiability of the bugs in ML-based systems and show the most important factors in each one. Then, we explore the challenges of generating a benchmark of bugs in ML-based software systems and provide a bug benchmark namely defect4ML that satisfies all criteria of standard benchmark, i.e. relevance, reproducibility, fairness, verifiability, and usability. This faultload benchmark contains 113 bugs reported by ML developers in GitHub and Stack Overflow, using two of the most popular ML frameworks: Tensor-Flow and Keras. defect4ML also addresses important challenges in Software Reliability Engineering of ML-based software systems, like: 1) fast changes in frameworks, by providing various bugs for different versions of frameworks, 2) code portability, by delivering similar bugs in different ML frameworks, 3) bug reproducibility, by providing fully reproducible bugs with complete information about required dependencies and data, and 4) lack of detailed information on bugs, by presenting links to the bugs' origins. defect4ML can be of interest to ML-based systems practitioners and researchers to assess their testing tools and techniques.

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“…Twelve code smells were proposed, including passing excessive objects, memory management mismatch, and so on [16]. Abidi et al subsequently proposed an approach to detect aforementioned anti-patterns and code smells (both called design smells according to the authors) in MPL systems in which Java Native Interface (JNI) is used, and conducted an empirical study on the fault proneness of such MPL design smells in nine open source JNI projects [9]. They found that MPL design smells are prevalent in the selected projects and files with MPL design smells can often be more associated with bugs than files without these design smells, and that specific smells are more correlated to fault proneness than other smells.…”

Section: B Quality Of Mpl Software Systemsmentioning

confidence: 99%

“…Another main reason is to take advantages of specific PLs to implement certain features, to meet various software quality needs, and to improve software development efficiency [1], [2], [3], [4], [5], [6], [7]. Nowadays, multi-programming-language (MPL) software development is increasingly prevalent with the technology advances [8], [4], [9]. This work was partially funded by the National Natural Science Foundation of China under Grant Nos.…”

Section: Introductionmentioning

confidence: 99%

Multi-Programming-Language Commits in OSS: An Empirical Study on Apache Projects

et al. 2021

Preprint

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Modern software systems, such as Spark, are usually written in multiple programming languages (PLs). Besides benefiting from code reuse, such systems can also take advantages of specific PLs to implement certain features, to meet various quality needs, and to improve development efficiency. In this context, a change to such systems may need to modify source files written in different PLs. We define a multi-programminglanguage commit (MPLC) in a version control system (e.g., Git) as a commit that involves modified source files written in two or more PLs. To our knowledge, the phenomenon of MPLCs in software development has not been explored yet. In light of the potential impact of MPLCs on development difficulty and software quality, we performed an empirical study to understand the state of MPLCs, their change complexity, as well as their impact on open time of issues and bug proneness of source files in real-life software projects. By exploring the MPLCs in 20 nontrivial Apache projects with 205,994 commits, we obtained the following findings: (1) 9% of the commits from all the projects are MPLCs, and the proportion of MPLCs in 80% of the projects goes to a relatively stable level; (2) more than 90% of the MPLCs from all the projects involve source files written in two PLs; (3) the change complexity of MPLCs is significantly higher than that of non-MPLCs in all projects; (4) issues fixed in MPLCs take significantly longer to be resolved than issues fixed in non-MPLCs in 80% of the projects; and (5) source files that have been modified in MPLCs tend to be more bug-prone than source files that have never been modified in MPLCs. These findings provide practitioners with useful insights on the architecture design and quality management of software systems written in multiple PLs.

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Are Multi-Language Design Smells Fault-Prone? An Empirical Study

Cited by 26 publications

References 55 publications

Understanding Bugs in Multi-Language Deep Learning Frameworks

Understanding Bugs in Multi-Language Deep Learning Frameworks

Bugs in Machine Learning-based Systems: A Faultload Benchmark

Multi-Programming-Language Commits in OSS: An Empirical Study on Apache Projects

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