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).
Tue 7 JulDisplayed time zone: (UTC) Coordinated Universal Time change
08:05 - 09:05 | I6-Empirical Studies and RequirementsJournal First / Software Engineering in Practice / Technical Papers at Silla Chair(s): Ita Richardson Lero - The Irish Software Research Centre and University of Limerick | ||
08:05 8mTalk | What do Programmers Discuss about Deep Learning FrameworksJ1 Journal First Junxiao Han Zhejiang University, Emad Shihab Concordia University, Zhiyuan Wan Zhejiang University, Shuiguang Deng Zhejiang University, Xin Xia Monash University | ||
08:13 12mTalk | Detection of Hidden Feature Requests from Massive Chat Messages via Deep Siamese NetworkTechnical Technical Papers Lin Shi ISCAS, Mingzhe Xing ISCAS, Mingyang Li ISCAS, Yawen Wang ISCAS, Shoubin Li ISCAS, Qing Wang Institute of Software, Chinese Academy of Sciences | ||
08:25 8mTalk | Recognizing lines of code violating company-specific coding guidelines using machine learningJ1 Journal First Miroslaw Ochodek Poznan University of Technology, Regina Hebig Chalmers University of Technology & University of Gothenburg, Wilhelm Meding Ericsson, Gert Frost Grundfos, Miroslaw Staron University of Gothenburg | ||
08:33 12mTalk | Context-aware In-process Crowdworker Recommendation Technical Papers Junjie Wang Institute of Software, Chinese Academy of Sciences, Ye Yang Stevens institute of technology, Song Wang York University, Yuanzhe Hu Institute of Software, Chinese Academy of Sciences, Dandan Wang Institute of Software, Chinese Academy of Sciences, Qing Wang Institute of Software, Chinese Academy of Sciences | ||
08:45 12mTalk | Using a Context-Aware Approach to Recommend Code Reviewers: Findings from an Industrial Case StudySEIP Software Engineering in Practice Anton Strand Ericsson AB, Markus Gunnarsson Ericsson AB, Ricardo Britto Ericsson / Blekinge Institute of Technology, Muhammad Usman Blekinge Institute of Technology | ||