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ICSE 2021
Sun 16 May - Sat 5 June 2021

This program is tentative and subject to change.

Genetic improvement uses artificial intelligence to automatically improve software with respect to non-functional properties (AI for SE). In this paper, we propose the use of existing software engineering best practice to enhance Genetic Improvement (SE for AI). We conjecture that existing Regression Test Selection (RTS) techniques (which have been proven to be efficient and effective) can and should be used as a core component of the GI search process for maximising its effectiveness. To assess our idea, we have carried out a thorough empirical study assessing the use of both dynamic and static RTS techniques with GI to improve seven real-world software programs. The results of our empirical evaluation show that incorporation of RTS within GI significantly speeds up the whole GI process, making it up to 78% faster on our benchmark set, being still able to produce valid software improvements. Our findings are significant in that they can save hours to days of computational time, and can facilitate the uptake of GI in an industrial setting, by significantly reducing the time for the developer to receive feedback from such an automated technique. Therefore, we recommend the use of RTS in future test-based automated software improvement work. Finally, we hope this successful application of SE for AI will encourage other researchers to investigate further applications in this area

This program is tentative and subject to change.

Tue 25 May
Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

12:05 - 13:05
1.2.2. Search-Based SE & Genetic OperationsTechnical Track / Journal-First Papers at Blended Sessions Room 2
Chair(s): José Miguel RojasUniversity of Leicester, UK
12:05
20m
Paper
Quality Indicators in Search-Based Software Engineering: An Empirical EvaluationJournal-First
Journal-First Papers
Shaukat AliSimula Research Laboratory, Norway, Paolo ArcainiNational Institute of Informatics , Dipesh PradhanSimula Research Laboratory, Norway, Safdar Aqeel SafdarSimula Research Laboratory, Norway, Tao YueNanjing University of Aeronautics and Astronautics
Link to publication DOI Authorizer link
12:25
20m
Paper
Utilizing Automatic Query Reformulations as Genetic Operations to Improve Feature Location in Software ModelsJournal-First
Journal-First Papers
Francisca PérezSVIT Research Group, Universidad San Jorge, Tewfik ZiadiLIP6, Sorbonne Université, Carlos CetinaSan Jorge University, Spain
Link to publication Pre-print
12:45
20m
Paper
Enhancing Genetic Improvement of Software with Regression Test SelectionArtifact ReusableTechnical TrackArtifact Available
Technical Track
Giovani GuizzoUniversity College London, Justyna PetkeUniversity College London, Federica SarroUniversity College London, Mark HarmanUniversity College London
Pre-print