Recognizing lines of code violating company-specific coding guidelines using machine learningJ1
Software developers in big and medium-size companies are working with millions of lines of code in their codebases. Assuring the quality of this code has shifted from simple defect management to proactive assurance of internal code quality. Although static code analysis and code reviews have been at the forefront of research and practice in this area, code reviews are still an effort-intensive and interpretation-prone activity. The aim of this research is to support code reviews by automatically recognizing company-specific code guidelines violations in large-scale, industrial source code. In our action research project, we constructed a machine-learning-based tool for code analysis where software developers and architects in big and medium-sized companies can use a few examples of source code lines violating code/design guidelines (up to 700 lines of code) to train decision-tree classifiers to find similar violations in their codebases (up to 3 million lines of code).
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08:05 - 09:05: I6-Empirical Studies and RequirementsPaper Presentations / Journal First / Software Engineering in Practice / Technical Papers at Silla Chair(s): Ita RichardsonLero - The Irish Software Research Centre and University of Limerick | |||
| 08:05 - 08:13 Talk | What do Programmers Discuss about Deep Learning FrameworksJ1 Journal First Junxiao HanZhejiang University, Emad ShihabConcordia University, Zhiyuan WanZhejiang University, Shuiguang DengZhejiang University, Xin XiaMonash University | ||
| 08:13 - 08:25 Talk | Detection of Hidden Feature Requests from Massive Chat Messages via Deep Siamese NetworkTechnical Technical Papers Lin ShiISCAS, Mingzhe XingISCAS, Mingyang LiISCAS, Yawen WangISCAS, Shoubin LiISCAS, Qing WangInstitute of Software, Chinese Academy of Sciences | ||
| 08:25 - 08:33 Talk | Recognizing lines of code violating company-specific coding guidelines using machine learningJ1 Journal First Miroslaw OchodekPoznan University of Technology, Regina HebigChalmers University of Technology & University of Gothenburg, Wilhelm MedingEricsson, Gert FrostGrundfos, Miroslaw StaronUniversity of Gothenburg | ||
| 08:33 - 08:45 Talk | Context-aware In-process Crowdworker Recommendation Technical Papers Junjie WangInstitute of Software, Chinese Academy of Sciences, Ye YangStevens institute of technology, Song WangYork University, Yuanzhe HuInstitute of Software, Chinese Academy of Sciences, Dandan WangInstitute of Software, Chinese Academy of Sciences, Qing WangInstitute of Software, Chinese Academy of Sciences | ||
| 08:45 - 08:57 Talk | Using a Context-Aware Approach to Recommend Code Reviewers: Findings from an Industrial Case StudySEIP Software Engineering in Practice Anton StrandEricsson AB, Markus GunnarssonEricsson AB, Ricardo BrittoEricsson / Blekinge Institute of Technology, Muhammad UsmanBlekinge Institute of Technology | ||