Paper Info
Title
A Cost-Effective Approach for Hyper-Parameter Tuning in Search-based Test Case Generation
Abstract
Search-based test case generation, which is the application of meta-heuristic search for generating test cases, has been studied a lot in the literature, lately. Since, in theory, the performance of meta-heuristic search methods is highly dependent on their hyper-parameters, there is a need to study hyper-parameter tuning in this domain. In this paper, we propose a new metric ("Tuning Gain"), which estimates how cost-effective tuning a particular class is. We then predict "Tuning Gain" using static features of source code classes. Finally, we prioritize classes for tuning, based on the estimated "Tuning Gains" and spend the tuning budget only on the highly-ranked classes. To evaluate our approach, we exhaustively analyze 1,200 hyper-parameter configurations of a well-known search-based test generation tool (EvoSuite) for 250 classes of 19 projects from benchmarks such as SF110 and SBST2018 tool competition. We used a tuning approach called Meta-GA and compared the tuning results with and without the proposed class prioritization. The results show that for a low tuning budget, prioritizing classes outperforms the alternatives in terms of extra covered branches (10 times more than a traditional global tuning). However, as the budget increases class selection will not be as effective, but still tuning in the class-level outperforms global tuning, by far.
Year
DOI
Venue
2020
10.1109/ICSME46990.2020.00047
2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)
Keywords
DocType
ISSN
Search-based Testing,Hyper-parameter Tuning,Test Case Generation,Source Code Metrics
Conference
1063-6773
ISBN
Citations 
PageRank 
978-1-7281-5620-0
0
0.34
References 
Authors
21
2
Name
Order
Citations
PageRank
Shayan Zamani130.72
Hadi Hemmati262227.54