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ICSE 2020
Wed 24 June - Thu 16 July 2020
Tue 7 Jul 2020 08:25 - 08:33 at Silla - I6-Empirical Studies and Requirements Chair(s): Ita Richardson

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 Jul
Times are displayed in time zone: (UTC) Coordinated Universal Time change

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
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
Detection of Hidden Feature Requests from Massive Chat Messages via Deep Siamese NetworkTechnicalArtifact Available
Technical Papers
Lin ShiISCAS, Mingzhe XingISCAS, Mingyang LiISCAS, Yawen WangISCAS, Shoubin LiISCAS, Qing WangInstitute of Software, Chinese Academy of Sciences
08:25 - 08:33
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
Context-aware In-process Crowdworker RecommendationACM SIGSOFT Distinguished Paper AwardsTechnical
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
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