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ICSE 2021
Mon 17 May - Sat 5 June 2021

Defect prediction can be a powerful tool to guide the use of quality assurance resources. However, while lots of research covered methods for defect prediction as well as methodological aspects of defect prediction research, the actual cost saving potential of defect prediction is still unclear. Within this article, we close this research gap and formulate a cost model for software defect prediction. We derive mathematically provable boundary conditions that must be fulfilled by defect prediction models such that there is a positive profit when the defect prediction model is used. Our cost model includes aspects like the costs for quality assurance, the costs of post-release defects, the possibility that quality assurance fails to reveal predicted defects, and the relationship between software artifacts and defects. We initialize the cost model using different assumptions, perform experiments to show trends of the behavior of costs on real projects. Our results show that the unrealistic assumption that defects only affect a single software artifact, which is a standard practice in the defect prediction literature, leads to inaccurate cost estimations. Moreover, the results indicate that thresholds for machine learning metrics are also not suited to define success criteria for software defect prediction.

Thu 27 May

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

11:50 - 13:05
3.2.3. Defect Prediction: Bug Characterization & AnalysisJournal-First Papers / NIER - New Ideas and Emerging Results at Blended Sessions Room 3 +12h
Chair(s): Robert Feldt Chalmers | University of Gothenburg, Blekinge Institute of Technology
11:50
20m
Paper
Watch out for Extrinsic Bugs! A Case Study of their Impact in Just-In-Time Bug Prediction Models on the OpenStack projectJournal-First
Journal-First Papers
Gema Rodríguez-Pérez University of Waterloo, Mei Nagappan University of Waterloo, Gregorio Robles Universidad Rey Juan Carlos
DOI Pre-print Media Attached
12:10
20m
Paper
An Empirical Study of Model-Agnostic Techniques for Defect Prediction ModelsJournal-First
Journal-First Papers
Jirayus Jiarpakdee Monash University, Australia, Chakkrit Tantithamthavorn Monash University, Hoa Khanh Dam University of Wollongong, John Grundy Monash University
Link to publication Pre-print Media Attached
12:30
20m
Paper
On the cost and profit of software defect predictionJournal-First
Journal-First Papers
Steffen Herbold University of Göttingen
Link to publication DOI Pre-print Media Attached
12:50
15m
Paper
Software Ticks Need No SpecificationsNIER
NIER - New Ideas and Emerging Results
Christoph Reichenbach Lund University
Pre-print Media Attached