A Pragmatic Approach for Hyper-Parameter Tuning in Search-based Test Case Generation
This work addresses the cost-effectiveness of tuning for software testing practitioners, but it is incremental as it builds on existing tuning methods.
The paper tackles the problem of hyper-parameter tuning in search-based test case generation by proposing a metric called 'Tuning Gain' to prioritize classes for tuning, resulting in 10 times more covered branches than traditional global tuning for low budgets.
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). In addition, we report the impact of different features of our approach such as search space size, tuning budgets, tuning algorithms, and the number of classes to tune, on the final results.