Watch out for this commit! A study of influential software changes

Li, Daoyuan; Li, Li; Kim, Dongsun; Bissyandé, Tegawendé F.; Lo, David; Traon, Yves Le

doi:10.1002/smr.2181

Cited by 9 publications

(6 citation statements)

References 93 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The competition aims at being a common playground on which the machine learning and the software engineering research communities can interact [14], with potential usages in the field of automated program repair [13]. The dataset is composed of five collections of source code files taken from real commits in open-source projects, belonging to seven different studies in the literature focusing on bug-fixes and change history analysis [28,46,55,71,79,95,97]. On the CodRep dataset we perform the same steps described in Section 2.2, which involves extracting the changed methods, abstracting the pairs, and selecting only small and medium methods.…”

Section: Rq1: Is Neural Machine Translation a Viable Approach To Learmentioning

confidence: 99%

An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

Tufano

Watson

Bavota

et al. 2019

ACM Trans. Softw. Eng. Methodol.

246

320

View full text Add to dashboard Cite

Millions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. First, we mine millions of bug-fixes from the change histories of projects hosted on GitHub, in order to extract meaningful examples of such bug-fixes. Next, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. In our empirical investigation we found that such a model is able to fix thousands of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9-50% of the cases, depending on the number of candidate patches we allow it to generate. Also, the model is able to emulate a variety of different Abstract Syntax Tree operations and generate candidate patches in a split second.

show abstract

Section: Rq1: Is Neural Machine Translation a Viable Approach To Learmentioning

confidence: 99%

An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

Tufano

Watson

Bavota

et al. 2019

ACM Trans. Softw. Eng. Methodol.

246

320

View full text Add to dashboard Cite

show abstract

“…Two specific approaches mentioned by the participants are commit analysis and package structure analysis . Through analyzing historical commits on version control systems, it is feasible to extract the relationship among source code files, among source code files and developers, and among developers 71 . Based on these relationships, the source code files could be organized (modularized) together.…”

Section: Resultsmentioning

confidence: 99%

Impacts, causes, and solutions of architectural smells in microservices: An industrial investigation

et al. 2022

View full text Add to dashboard Cite

As a recently predominant architecture style, MicroService Architecture (MSA) is likely to suffer the issues of poor maintainability due to inappropriate microservice boundaries. Architectural Smell (AS), as a metaphor for potential architectural issues that may have negative impacts on software maintenance, can be used to pinpoint refactoring opportunity for evolving microservice boundary. However, existing studies mostly focus on AS detection with little further investigation on the possible impacts, causes, and solutions of AS, which does little help in addressing the bad smells in architecture. Our goal in this study is to bridge this gap by investigating the possible impacts, causes, and solutions of AS in MSA-based systems. An industrial case study is carried out to collect repository data and practitioners' views on six typical ASes in a real MSA-based telecommunication system. Statistical Analysis and Coding techniques are used in the analyses of quantitative and qualitative data respectively.The results show that AS influences the modularity, modifiability, analyzability, and testability of the MSA-based system, which further induce extra cross-team communication, change-and fault-prone microservices. To explore the causes for AS, a five-aspect conceptual classification with technology, project, organization, business, and professional is proposed, in which the business and organization aspects take the major roles. Both technical and non-technical solutions are distilled to deal with ASes despite potential constraints. These results and their comparison to current literature are discussed, which provide practical implications in coping with AS in microservices.

show abstract

“…In parallel to our research, Li et al investigate influential software changes and propose categories to identify them in an automated fashion using machine learning classification techniques. Although they propose similar metrics that could also help developers to reduce the amount of changes they need to inspect, their goal, study design, and technical approach are different from ours.…”

Section: Related Workmentioning

confidence: 99%

Identifying unusual commits on GitHub

Goyal

Ferreira

Kästner

et al. 2017

J Software Evolu Process

View full text Add to dashboard Cite

Transparent environments and social-coding platforms as GitHub help developers to stay abreast of changes during the development and maintenance phase of a project. Especially, notification feeds can help developers to learn about relevant changes in other projects. Unfortunately, transparent environments can quickly overwhelm developers with too many notifications, such that they lose the important ones in a sea of noise. Complementing existing prioritization and filtering strategies based on binary compatibility and code ownership, we develop an anomaly detection mechanism to identify unusual commits in a repository, which stand out with respect to other changes in the same repository or by the same developer. Among others, we detect exceptionally large commits, commits at unusual times, and commits touching rarely changed file types given the characteristics of a particular repository or developer. We automatically flag unusual commits on GitHub through a browser plug-in. In an interactive survey with 173 active GitHub users, rating commits in a project of their interest, we found that, although our unusual score is only a weak predictor of whether developers want to be notified about a commit, information about unusual characteristics of a commit changes how developers regard commits. Our anomaly detection mechanism is a building block for scaling transparent environments. KEYWORDSanomaly detection, information overload, notification feeds, software ecosystems, transparent environments INTRODUCTIONCollaborative development in open source, software ecosystems, and also industrial software systems relies increasingly on decentralized decision making. 1-4 Interdependent components evolve independently and often with little explicit collaboration. Backward-incompatible changes that break modularity and produce rippling effects on downstream components are often necessary to avoid opportunity costs (not fixing mistakes, stifling change in the face of evolving requirements) and common in practice. [5][6][7][8][9][10][11][12][13][14][15] In addition, components may change to add new functionality that developers might want to adopt. Identifying relevant changes and reacting to them if needed can create a significant burden on developers during maintenance. 10,[15][16][17][18][19][20] Seeds of a solution can be found in today's transparent environments or social-coding platforms such as GitHub, LaunchPad, and Bitbucket. These environments provide mechanisms for notification and exploration, that help developers to stay abreast of activities across collections of projects without central planning. 21,22 For example, on GitHub, developers can watch projects and receive a notification feed of activities in watched projects, such as push events or bug reports. These tools work well at small scales but break down for large projects where imprecise and insufficiently rich notification mechanisms lead to information overload from notification cluttering. By inspecting publicly available events on GitHub, we found t...

show abstract

Watch out for this commit! A study of influential software changes

Cited by 9 publications

References 93 publications

An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

Impacts, causes, and solutions of architectural smells in microservices: An industrial investigation

Identifying unusual commits on GitHub

Contact Info

Product

Resources

About