How Different Is It Between Machine-Generated and Developer-Provided Patches? : An Empirical Study on the Correct Patches Generated by Automated Program Repair Techniques

Wang, Shangwen; Wen, Ming; Chen, Liqian; Yi, Xin; Mao, Xiaoguang

doi:10.1109/esem.2019.8870172

Cited by 30 publications

(17 citation statements)

References 62 publications

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“…Thus, the reputation of a system influences the extent to which humans trust and use that system. Additionally, in the computer science literature, researchers have explored differences in correctness perceptions of machine-generated and human-generated patches [32,33], although this research focused on correctness rather than trust in the system. Trust is inherently different as it refers to the willingness to be vulnerable to the system, rather than the correctness of each patch.…”

Section: Code Reputation and Cognitive Biasesmentioning

confidence: 99%

Would You Fix This Code for Me? Effects of Repair Source and Commenting on Trust in Code Repair

Alarcon

Walter

Gibson

et al. 2020

Systems

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Automation and autonomous systems are quickly becoming a more engrained aspect of modern society. The need for effective, secure computer code in a timely manner has led to the creation of automated code repair techniques to resolve issues quickly. However, the research to date has largely ignored the human factors aspects of automated code repair. The current study explored trust perceptions, reuse intentions, and trust intentions in code repair with human generated patches versus automated code repair patches. In addition, comments in the headers were manipulated to determine the effect of the presence or absence of comments in the header of the code. Participants were 51 programmers with at least 3 years’ experience and knowledge of the C programming language. Results indicated only repair source (human vs. automated code repair) had a significant influence on trust perceptions and trust intentions. Specifically, participants consistently reported higher levels of perceived trustworthiness, intentions to reuse, and trust intentions for human referents compared to automated code repair. No significant effects were found for comments in the headers.

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Section: Code Reputation and Cognitive Biasesmentioning

confidence: 99%

Would You Fix This Code for Me? Effects of Repair Source and Commenting on Trust in Code Repair

Alarcon

Walter

Gibson

et al. 2020

Systems

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“…Le et al [48] assessed the reliability of these two methods and found that a notable part of patches passing the independent test suite are still incorrect while manual validation can suffer from subjectivity. Wang et al [71] further dissected the differences between machinegenerated patches and ground-truth to provide guidance for future manual assessment. Liu et al [21] concluded totally ten common code change patterns from correct patches for further easing this process.…”

Section: Related Workmentioning

confidence: 99%

On the Impact of Flaky Tests in Automated Program Repair

Qin

Wang

Liu

et al. 2021

2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

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“…To generate repair patches as simple as possible, has already mentioned in many works [15], [26], [32]. According to a recent study [16] 25.4% (45/177) of the correct patches generated by APR techniques are syntactically different from developer provided ones. Other approaches also exists, which generate patches by learning human-written program codes [33], [34].…”

Section: Related Workmentioning

confidence: 99%

Exploring Plausible Patches Using Source Code Embeddings in JavaScript

Csuvik¹,

Horváth²,

Lajkó³

et al. 2021

Preprint

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Despite the immense popularity of the Automated Program Repair (APR) field, the question of patch validation is still open. Most of the present-day approaches follow the socalled Generate-and-Validate approach, where first a candidate solution is being generated and after validated against an oracle. The latter, however, might not give a reliable result, because of the imperfections in such oracles; one of which is usually the test suite. Although (re-) running the test suite is right under one's nose, in real life applications the problem of over-and underfitting often occurs, resulting in inadequate patches. Efforts that have been made to tackle with this problem include patch filtering, test suite expansion, careful patch producing and many more. Most approaches to date use post-filtering relying either on test execution traces or make use of some similarity concept measured on the generated patches. Our goal is to investigate the nature of these similarity-based approaches. To do so, we trained a Doc2Vec model on an open-source JavaScript project and generated 465 patches for 10 bugs in it. These plausible patches alongside with the developer fix are then ranked based on their similarity to the original program. We analyzed these similarity lists and found that plain document embeddings may lead to misclassification -it fails to capture nuanced code semantics. Nevertheless, in some cases it also provided useful information, thus helping to better understand the area of Automated Program Repair.

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How Different Is It Between Machine-Generated and Developer-Provided Patches? : An Empirical Study on the Correct Patches Generated by Automated Program Repair Techniques

Cited by 30 publications

References 62 publications

Would You Fix This Code for Me? Effects of Repair Source and Commenting on Trust in Code Repair

Would You Fix This Code for Me? Effects of Repair Source and Commenting on Trust in Code Repair

On the Impact of Flaky Tests in Automated Program Repair

Exploring Plausible Patches Using Source Code Embeddings in JavaScript

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