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ICSE 2021
Mon 17 May - Sat 5 June 2021

Finding and fixing buggy code is an important and cost-intensive maintenance task, and static analysis (SA) is one of the methods developers use to perform it. SA tools warn developers about potential bugs by scanning their source code for commonly occurring bug patterns, thus giving those developers opportunities to fix the warnings (potential bugs) before they release the software. Typically, SA tools scan for general bug patterns that are common to any software project (such as null pointer dereference), and not for project-specific patterns. However, past research has pointed to this lack of customizability as a severe limiting issue in SA. Accordingly, in this paper, we propose an approach called Ammonia, which is based on statically analyzing changes across the development history of a project, as a means to identify project-specific bug patterns. Furthermore, the bug patterns identified by our tool do not relate to just one developer or one specific commit, they reflect the project as a whole and complement the warnings from other SA tools that identify general bug patterns. Herein, we report on the application of our implemented tool and approach to four Java projects: Ant, Camel, POI, and Wicket. The results obtained show that our tool could detect 19 project-specific bug patterns across those four projects. Next, through manual analysis, we determined that six of those change patterns were actual bugs and submitted pull requests based on those bug patterns. As a result, five of the pull requests were merged.

Conference Day
Thu 27 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

10:00 - 11:00
3.1.3. Defect Prediction: Automation #2Journal-First Papers at Blended Sessions Room 3 +12h
Chair(s): Robert FeldtChalmers | University of Gothenburg, Blekinge Institute of Technology
10:00
20m
Paper
Revisiting Supervised and Unsupervised Methods for Effort-Aware Cross-Project Defect PredictionJournal-First
Journal-First Papers
Chao NiZhejiang University, Xin XiaHuawei Software Engineering Application Technology Lab, David LoSingapore Management University, Xiang ChenNantong University, Qing GuNanjing University
Pre-print Media Attached
10:20
20m
Paper
Ammonia: an Approach for Deriving Project-Specific Bug PatternsJournal-First
Journal-First Papers
Yoshiki HigoOsaka University, Shinpei HayashiTokyo Institute of Technology, Hideaki HataShinshu University, Mei NagappanUniversity of Waterloo
Link to publication DOI Authorizer link Pre-print Media Attached
10:40
20m
Paper
Predicting Defective Lines Using a Model-Agnostic TechniqueJournal-First
Journal-First Papers
Supatsara WattanakriengkraiNara Institute of Science and Technology, Patanamon ThongtanunamUniversity of Melbourne, Chakkrit TantithamthavornMonash University, Hideaki HataShinshu University, Kenichi MatsumotoNara Institute of Science and Technology
DOI Pre-print Media Attached
22:00 - 23:00
3.1.3. Defect Prediction: Automation #2Journal-First Papers at Blended Sessions Room 3
22:00
20m
Paper
Revisiting Supervised and Unsupervised Methods for Effort-Aware Cross-Project Defect PredictionJournal-First
Journal-First Papers
Chao NiZhejiang University, Xin XiaHuawei Software Engineering Application Technology Lab, David LoSingapore Management University, Xiang ChenNantong University, Qing GuNanjing University
Pre-print Media Attached
22:20
20m
Paper
Ammonia: an Approach for Deriving Project-Specific Bug PatternsJournal-First
Journal-First Papers
Yoshiki HigoOsaka University, Shinpei HayashiTokyo Institute of Technology, Hideaki HataShinshu University, Mei NagappanUniversity of Waterloo
Link to publication DOI Authorizer link Pre-print Media Attached
22:40
20m
Paper
Predicting Defective Lines Using a Model-Agnostic TechniqueJournal-First
Journal-First Papers
Supatsara WattanakriengkraiNara Institute of Science and Technology, Patanamon ThongtanunamUniversity of Melbourne, Chakkrit TantithamthavornMonash University, Hideaki HataShinshu University, Kenichi MatsumotoNara Institute of Science and Technology
DOI Pre-print Media Attached