检索结果-内蒙古大学图书馆

Recommending pull request reviewers based on code changes

SOFT COMPUTING 2021年第7期25卷 5619-5632页

作者： Ye, Xin Zheng, Yongjie Aljedaani, Wajdi Mkaouer, Mohamed Wiem Calif State Univ San Marcos TX 92096 USA Univ North Texas Denton TX 76203 USA Rochester Inst Technol Rochester NY 14623 USA

Pull-based development supports collaborative distributed development. It enables developers to collaborate on projects hosted on GitHub. If a developer wants to collaborate on a project, he/she will fork the repository, make modifications on the forked repository and send a pull request to the development team to ask for a merge of the code changes to the official repository. When the development team receives a pull request, the team members will review the changes and make a decision on whether to accept the changes or not. However, efficiently finding suitable pull request reviewers is a challenge. In this paper, we propose a multi-instance-based deep neural network model to recommend reviewers for pull requests. Given a pull request, our model extracts three features, which pull request title, commit message, and code change. The proposed model extracts the three features automatically from the code changes of every commit in the pull request. The features of different commits are then merged to predict the likelihood that a reviewer candidate is the appropriate reviewer. We use CNN and LSTM-network to learn features since the pull requisition and commit message feature have different structures than code change, written in a programming language. To test the effectiveness of our model, we performed a set of experiments using 43,986 pull requests extracted from 12 open-source projects. We compare our model with two baselines approaches, CoreDevRec and Majority Classes. Experiments demonstrate that our model outperforms two state-of-the-art baselines. For instance, for the TensorFlow project, our model's accuracy in determining the appropriate reviewers is 50.80%, 74.70%, and 84.04%, respectively, in Top-1, Top-3, and Top-5 recommendation.

关键词： Pull request Reviewer recommendation code changes Artificial neural network

来源：评论

学校读者我要写书评

暂无评论

Query expansion based on statistical learning from code changes

引用

SOFTWARE-PRACTICE & EXPERIENCE 2018年第7期48卷 1333-1351页

作者： Huang, Qing Yang, Yangrui Zhan, Xue Wan, Hongyan Wu, Guoqing Wuhan Univ Comp Sch State Key Lab Software Engn Wuhan Hubei Peoples R China North China Univ Water Resources & Elect Power Coll Informat Engn Zhengzhou Henan Peoples R China Deepin Technol Co Ltd Wuhan Hubei Peoples R China

Thesaurus-based, code-related, and software-specific query expansion techniques are the main contributions in free-form query search. However, these techniques still could not put the most relevant query result in the first position because they lack the ability to infer the expansion words that represent the user needs based on a given query. In this paper, we discover that code changes can imply what users want and propose a novel query expansion technique with code changes (QECC). It exploits (changes, contexts) pairs from changed methods. On the basis of statistical learning from pairs, it can infer code changes for a given query. In this way, it expands a query with code changes and recommends the query results that meet actual needs perfectly. In addition, we implement InstaRec to perform QECC and evaluate it with 195039 change commits from GitHub and our code tracker. The results show that QECC can improve the precision of 3 code search algorithms (ie, IR, Portfolio, and VF) by up to 52% to 62% and outperform the state-of-the-art query expansion techniques (ie, query expansion based on crowd knowledge and codeHow) by 13% to 16% when the top 1 result is inspected.

关键词： code changes code search information retrieval software reuse statistical learning query expansion

来源：评论

学校读者我要写书评

暂无评论

How different are different diff algorithms in Git? Use --histogram for code changes

引用

EMPIRICAL SOFTWARE ENGINEERING 2020年第1期25卷 790-823页

作者： Nugroho, Yusuf Sulistyo Hata, Hideaki Matsumoto, Kenichi Nara Inst Sci Technol Ikoma Japan

Automatic identification of the differences between two versions of a file is a common and basic task in several applications of mining code repositories. Git, a version control system, has a diff utility and users can select algorithms of diff from the default algorithm Myers to the advanced Histogram algorithm. From our systematic mapping, we identified three popular applications of diff in recent studies. On the impact on code churn metrics in 14 Java projects, we obtained different values in 1.7% to 8.2% commits based on the different diff algorithms. Regarding bug-introducing change identification, we found 6.0% and 13.3% in the identified bug-fix commits had different results of bug-introducing changes from 10 Java projects. For patch application, we found that the Histogram is more suitable than Myers for providing the changes of code, from our manual analysis. Thus, we strongly recommend using the Histogram algorithm when mining Git repositories to consider differences in source code.

关键词： code changes Diff Histogram algorithm Mining repositories

来源：评论

学校读者我要写书评

暂无评论

Change impact graphs: Determining the impact of prior code changes

引用

INFORMATION AND SOFTWARE TECHNOLOGY 2009年第10期51卷 1394-1408页

作者： German, Daniel M. Hassan, Ahmed E. Robles, Gregorio Univ Victoria Dept Comp Sci Victoria BC V8W 2Y2 Canada Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

The source code of a software system is in constant change. The impact of these changes spreads out across the software system and may lead to the sudden manifestation of failures in unchanged parts. To help developers fix such failures, we propose a method that, in a pre-processing stage. analyzes prior code changes to determine what functions have been modified. Next, given a particular period of time in the past, the functions changed during that period are propagated throughout the rest of the system using the dependence graph of the system. This information is visualized using Change Impact Graphs (CIGs). Through a case study based on the Apache Web Server, we demonstrate the benefit of using CIGs to investigate several real defects. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Change impact graph Defect detection code changes Software evolution

来源：评论

学校读者我要写书评

暂无评论

Change impact graphs: Determining the impact of prior code changes

Change impact graphs: Determining the impact of prior code c...

引用

8th IEEE International Working Conference on Source code Analysis and Manipulation

作者： German, Daniel M. Hassan, Ahmed E. Robles, Gregorio Univ Victoria Dept Comp Sci Victoria BC V8W 2Y2 Canada Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

ISBN: (纸本)9780769533537

关键词： Change impact graph Defect detection code changes Software evolution

来源：评论

学校读者我要写书评

暂无评论

On Automatically Generating Commit Messages via Summarization of Source code changes 14

On Automatically Generating Commit Messages via Summarizatio...

引用

14th IEEE International Working Conference on Source code Analysis and Manipulation (SCAM)

作者： Fernando Cortes-Coy, Luis Linares-Vasquez, Mario Aponte, Jairo Poshyvanyk, Denys Univ Nacl Colombia Bogota Colombia Coll William & Mary Williamsburg VA USA

ISBN: (纸本)9780769553047

Although version control systems allow developers to describe and explain the rationale behind code changes in commit messages, the state of practice indicates that most of the time such commit messages are either very short or even empty. In fact, in a recent study of 23K+ Java projects it has been found that only 10% of the messages are descriptive and over 66% of those messages contained fewer words as compared to a typical English sentence (i.e., 15-20 words). However, accurate and complete commit messages summarizing software changes are important to support a number of development and maintenance tasks. In this paper we present an approach, coined as changescribe, which is designed to generate commit messages automatically from change sets. changescribe generates natural language commit messages by taking into account commit stereotype, the type of changes (e.g., files rename, changes done only to property files), as well as the impact set of the underlying changes. We evaluated changescribe in a survey involving 23 developers in which the participants analyzed automatically generated commit messages from real changes and compared them with commit messages written by the original developers of six open source systems. The results demonstrate that automatically generated messages by changescribe are preferred in about 62% of the cases for large commits, and about 54% for small commits.

关键词： Commit message summarization code changes

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Learning of General-Purpose Embeddings for code changes 5

Unsupervised Learning of General-Purpose Embeddings for Code...

引用

5th International Workshop on Machine Learning Techniques for Software Quality Evolution (MaLTESQuE)

作者： Pravilov, Mikhail Bogomolov, Egor Golubev, Yaroslav Bryksin, Timofey Higher Sch Econ St Petersburg Russia JetBrains Res St Petersburg Russia

ISBN: (纸本)9781450386258

Applying machine learning to tasks that operate with code changes requires their numerical representation. In this work, we propose an approach for obtaining such representations during pre-training and evaluate them on two different downstream tasks - applying changes to code and commit message generation. During pretraining, the model learns to apply the given code change in a correct way. This task requires only code changes themselves, which makes it unsupervised. In the task of applying code changes, our model outperforms baseline models by 5.9 percentage points in accuracy. As for the commit message generation, our model demonstrated the same results as supervised models trained for this specific task, which indicates that it can encode code changes well and can be improved in the future by pre-training on a larger dataset of easily gathered code changes.

关键词： code changes Unsupervised learning Commit message generation

来源：评论

学校读者我要写书评

暂无评论

Automatic Clustering of code changes 13

Automatic Clustering of Code Changes

引用

13th International Conference on Mining Software Repositories (MSR)

作者： Kreutzer, Patrick Dotzler, Georg Ring, Matthias Eskofier, Bjoern M. Philippsen, Michael Friedrich Alexander Univ Erlangen Nurnberg FAU Programming Syst Grp Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Digital Sports Grp Erlangen Germany

ISBN: (纸本)9781450341868

Several research tools and projects require groups of similar code changes as input. Examples are recommendation and bug finding tools that can provide valuable information to developers based on such data. With the help of similar code changes they can simplify the application of bug fixes and code changes to multiple locations in a project. But despite their bene fit, the practical value of existing tools is limited, as users need to manually specify the input data, i.e., the groups of similar code changes. To overcome this drawback, this paper presents and evaluates two syntactical similarity metrics, one of them is specifically designed to run fast, in combination with two carefully selected and self-tuning clustering algorithms to automatically detect groups of similar code changes. We evaluate the combinations of metrics and clustering algorithms by applying them to several open source projects and also publish the detected groups of similar code changes online as a reference dataset. The automatically detected groups of similar code changes work well when used as input for LASE, a recommendation system for code changes.

关键词： Clustering code changes Software Repositories

来源：评论

学校读者我要写书评

暂无评论

Recommending code Reviews Leveraging code changes with Structured Information Retrieval 39

Recommending Code Reviews Leveraging Code Changes with Struc...

引用

39th IEEE International Conference on Software Maintenance and Evolution (ICSME)

作者： Shuvo, Ohiduzzaman Mahbub, Parvez Rahman, Mohammad Masudur Dalhousie Univ Fac Comp Sci Halifax NS Canada

ISBN: (纸本)9798350327830

Review comments are one of the main building blocks of modern code reviews. Manually writing code review comments could be time-consuming and technically challenging. Recently, an information retrieval (IR) based approach has been proposed to automatically recommend relevant code review comments for method-level code changes. However, this technique overlooks the structured items (e.g., class name, library information) from the source code and is applicable only for method-level changes. In this paper, we propose a novel technique for relevant review comments recommendation - RevCom - that leverages various code-level changes using structured information retrieval. RevCom uses different structured items from source code and can recommend relevant reviews for all types of changes (e.g., method-level and non-method-level). Our evaluation using three performance metrics show that RevCom outperforms both IR-based and DL-based baselines by up to 49.45% and 23.57% margins in BLEU score in recommending review comments. We find that RevCom can recommend review comments with an average BLEU score of approximate to 26.63%. According to Google's AutoML Translation documentation, such a BLEU score indicates that the review comments can capture the original intent of the reviewers. All these findings suggest that RevCom can recommend relevant code reviews and has the potential to reduce the cognitive effort of human code reviewers.

关键词： Software Engineering code Reviews code changes Structured Information Retrieval

来源：评论

学校读者我要写书评

暂无评论

How Do code changes Evolve in Different Platforms? A Mining-based Investigation

How Do Code Changes Evolve in Different Platforms? A Mining-...

引用

35th IEEE International Conference on Software Maintenance and Evolution (ICSME)

作者： Viggiato, Markos Oliveira, Johnatan Figueiredo, Eduardo Jamshidi, Pooyan Kastner, Christian Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada Univ Fed Minas Gerais Comp Sci Dept Belo Horizonte MG Brazil Univ South Carolina Comp Sci & Engn Dept Columbia SC 29208 USA Carnegie Mellon Univ Inst Software Res Pittsburgh PA 15213 USA

ISBN: (纸本)9781728130941

code changes are performed differently in the mobile and non-mobile platforms. Prior work has investigated the differences in specific platforms. However, we still lack a deeper understanding of how code changes evolve across different software platforms. In this paper, we present a study aiming at investigating the frequency of changes and how source code, build and test changes co-evolve in mobile and non-mobile platforms. We developed regression models to explain which factors influence the frequency of changes and applied the Apriori algorithm to find types of changes that frequently co-occur. Our findings show that non-mobile repositories have a higher number of commits per month and our regression models suggest that being mobile significantly impacts on the number of commits in a negative direction when controlling for confound factors, such as code size. We also found that developers do not usually change source code files together with build or test files. We argue that our results can provide valuable information for developers on how changes are performed in different platforms so that practices adopted in successful software systems can be followed.

关键词： Software evolution code changes platforms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：