Automatically documenting program changes

Buse, Raymond P.L.; Weimer, Westley

doi:10.1145/1858996.1859005

Cited by 163 publications

(111 citation statements)

References 35 publications

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“…They presented patches to participants and asked maintainability related questions developed by Sillito, Murphy, and Volder [25]. In addition, they presented machine-generated change documents [26] along with patches to participants. They found that machine-generated patches [8] with machine-generated documents [26] are comparable to human-written patches in terms of maintainability.…”

Section: B Threats To Validitymentioning

confidence: 99%

Automatic patch generation learned from human-written patches

Kim

Nam

Song

et al. 2013

2013 35th International Conference on Software Engineering (ICSE)

448

612

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Abstract-Patch generation is an essential software maintenance task because most software systems inevitably have bugs that need to be fixed. Unfortunately, human resources are often insufficient to fix all reported and known bugs. To address this issue, several automated patch generation techniques have been proposed. In particular, a genetic-programming-based patch generation technique, GenProg, proposed by Weimer et al., has shown promising results. However, these techniques can generate nonsensical patches due to the randomness of their mutation operations.To address this limitation, we propose a novel patch generation approach, Pattern-based Automatic program Repair (PAR), using fix patterns learned from existing human-written patches. We manually inspected more than 60,000 human-written patches and found there are several common fix patterns. Our approach leverages these fix patterns to generate program patches automatically.

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Section: B Threats To Validitymentioning

confidence: 99%

Automatic patch generation learned from human-written patches

Kim

Nam

Song

et al. 2013

2013 35th International Conference on Software Engineering (ICSE)

448

612

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“…Recent work from Sridhara et al automatically synthesize natural language sentences for specific types of code segments directly, including Java methods [3], groups of statements [4], Java classes [5], and parameter comments [6]. Others focus on generating documents for exception [22], failed test cases [23], and code changes [24]. A few previous work focus on mining descriptions or documentation from developer communications, such as bug reports, forum posts and emails [25], [26], [27].…”

Section: A Automatic Comment Generationmentioning

confidence: 99%

CloCom: Mining existing source code for automatic comment generation

Wong

Liu

Tan

2015

2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

133

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Abstract-Code comments are an integral part of software development. They improve program comprehension and software maintainability. The lack of code comments is a common problem in the software industry. Therefore, it is beneficial to generate code comments automatically. In this paper, we propose a general approach to generate code comments automatically by analyzing existing software repositories. We apply code clone detection techniques to discover similar code segments and use the comments from some code segments to describe the other similar code segments. We leverage natural language processing techniques to select relevant comment sentences.In our evaluation, we analyze 42 million lines of code from 1,005 open source projects from GitHub, and use them to generate 359 code comments for 21 Java projects. We manually evaluate the generated code comments and find that only 23.7% of the generated code comments are good. We report to the developers the good code comments, whose code segments do not have an existing code comment. Amongst the reported code comments, seven have been confirmed by the developers as good and committable to the software repository while the rest await for developers' confirmation. Although our approach can generate good and committable comments, we still have to improve the yield and accuracy of the proposed approach before it can be used in practice with full automation.

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“…Such tools have generally focused on automatically generating documentation where none exists [12,13,58,59]. Preliminary studies have focused on very specific classes of incomplete or inconsistent comments (specifically, only for Javadoc elements [55] and less than 30 comments each in large, open source programs [63,64]), but there exists no general approach for identifying low-quality comments of arbitrary types.…”

Section: Ensuring Documentation Completeness and Consistencymentioning

confidence: 99%

“…Studies suggest that only around half of all methods in realworld systems are commented in practice [34]. Several tools have been developed to generate documentation where none exists [12,13,58,59]. While research suggests that up to 34% of existing comments may fail to completely document the associated code, relatively little work has been done to identify or remedy such situations [55].…”

Section: Background and Related Workmentioning

confidence: 99%

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Leveraging Light-Weight Analyses to Aid Software Maintenance

Fry

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Software maintenance can account for up to 90% of a system's life cycle cost. As a result, many automated techniques have been developed to reduce the overall effort necessary to sustain software over time. While many of these tools work well under certain circumstances, we believe that they could be improved by taking advantage of large untapped sources of unstructured information that result from natural software development. As the size and complexity of systems increases, taking advantage of such previously-overlooked information is of paramount importance.The proposed research will design lightweight analyses to extract latent information encoded by humans in software development artifacts and thereby reduce the costs of software maintenance. We will design analyses that apply throughout the maintenance process, focusing on three areas: (1) triaging large collections of exposed defects; (2) guiding the search for automatic defect repairs; and (3) ensuring the continued consistency of system documentation over time.For each area of proposed research we aim to improve upon existing approaches to reduce the total cost of software maintenance while requiring minimal additional developer effort. We will evaluate the proposed work using these criteria on a diverse set of real world programs totaling millions of lines of code to concretely show the reduction of maintenance cost and improvement upon existing processes.

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Automatically documenting program changes

Cited by 163 publications

References 35 publications

Automatic patch generation learned from human-written patches

Automatic patch generation learned from human-written patches

CloCom: Mining existing source code for automatic comment generation

Leveraging Light-Weight Analyses to Aid Software Maintenance

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