Mining Python fix patterns via analyzing fine-grained source code changes

Yang, Yilin; He, Tianxing; Feng, Yang; Liu, Shaoying; Xu, Baowen

doi:10.1007/s10664-021-10087-1

Cited by 10 publications

(2 citation statements)

References 55 publications

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“…To label repositories into ML-based or non-ML-based, the first three authors classified the first 15 repositories of each ML framework (45 in total) independently. To assess inter-rater agreement among them, we used Fleiss' kappa [52] like in similar works [53,54], and obtained an inter-rater agreement of about 33%. Next, the three authors meet to discuss the conflicts and resolve them.…”

Section: Manual Inspection Of Repositoriesmentioning

confidence: 99%

Bugs in machine learning-based systems: a faultload benchmark

et al. 2023

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Rapid growth of applying Machine Learning (ML) in different domains, especially in safety-critical areas, increases the need for reliable ML components, i.e., a software component operating based on ML. Since corrective maintenance, i.e. identifying and resolving systems bugs, is a key task in the software development process to deliver reliable software components, it is necessary to investigate the usage of ML components, from the software maintenance perspective. Understanding the bugs characteristics and maintenance challenges in ML-based systems can help developers of these systems to identify where to focus maintenance and testing efforts, by giving insights into the most error-prone components, most common bugs, etc. In this paper, we investigate the characteristics of bugs in ML-based software systems and the difference between ML and non-ML bugs from the maintenance viewpoint. We extracted 447,948 GitHub repositories that used one of the three most popular ML frameworks, i.e., TensorFlow, Keras, and PyTorch. After multiple filtering steps, we select the top 300 repositories with the highest number of closed issues. We manually investigate the extracted repositories to exclude non-MLbased systems. Our investigation involved a manual inspection of 386 sampled reported issues in the identified ML-based systems to indicate whether they affect ML components or not. Our analysis shows that nearly half of the real

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Section: Manual Inspection Of Repositoriesmentioning

confidence: 99%

Bugs in machine learning-based systems: a faultload benchmark

et al. 2023

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“…The QuixBugs dataset does not contain large scale programs; therefore, they may not be realistic bug fix changes. However, QuixBugs has been used extensively in automatic program repair [10], [29], [30], [36] to learn the characteristics of bug fix changes. With the QuixBugs dataset and benchmark, these previous studies were able to achieve successful automatic repairing results.…”

Section: B Threats To Validitymentioning

confidence: 99%

Tracking Code Bug Fix Ripple Effects Based on Change Patterns Using Markov Chain Models

2022

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Change impact analysis evaluates the changes that are made in the software and finds the ripple effects, in other words, finds the affected software components. Code changes and bug fixes can have a high impact on code quality by introducing new vulnerabilities or increasing their severity. A recent high-visibility example of this is the code changes in the log4j web software CVE-2021-45105 to fix known vulnerabilities by removing and adding method called change types. This bug fix process exposed further code security concerns. In this article, we analyze the most common set of bug fix change patterns to have a better understanding of the distribution of software changes and their impact on code quality. To achieve this, we implemented a tool that compares two versions of the code and extracts the changes that have been made. Then, we investigated how these changes are related to change impact analysis. In our case study, we identified the change types for bug-inducing and bug fix changes using the Quixbugs dataset. Furthermore, we used 13 of the projects and 621 bugs from Defects4J to identify the common change types in bug fixes. Then, to find the change types that cause an impact on the software, we performed an impact analysis on a subset of projects and bugs of Defects4J. The results have shown that, on average, 90% of the bug fix change types are adding a new method declaration and changing the method body. Then, we investigated if these changes cause an impact or a ripple effect in the software by performing a Markov chain-based change impact analysis. The results show that the bug fix changes had only impact rates within a range of 0.4-5%. Furthermore, we performed a statistical correlation analysis to find if any of the bug fixes have a significant correlation with the impact of change. The results have shown that there is a negative correlation between caused impact with the change types adding new method declaration and changing method body. On the other hand, we found that there is a positive correlation between caused impact and changing the field type.

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