Effective Software Mutation-Test Using Program Instructions Classification
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The quantity of bugs that a software test-data finds determines its effectiveness. A useful technique for assessing the efficacy of a test set is mutation testing. The primary issues with the mutation test are cost and time requirements. Close to 40% of the injected bugs in the mutation test are effect-less (equivalent). Reducing the number of generated total mutants by decreasing equivalent mutants and reducing the execution time of the mutation test are the main objectives of this study. An error-propagation aware mutation test approach has been suggested in this research. Three steps make up the process. To find a collection of instruction-level characteristics effective on the error propagation rate, the data and instructions of the input program were evaluated in the first step. Utilizing supervised machine learning techniques, an instruction classifier was developed using the prepared dataset in the second step. After classifying the program instructions automatically by the created classifier, the mutation test is performed only on the identified error-propagating instructions; the identified non-error-propagating instructions are avoided to mutate in the proposed mutation testing. The conducted experiments on the set of standard benchmark programs indicate that the proposed method causes about 19% reduction in the number of generated mutants. Furthermore, the proposed method causes a 32.24% reduction in the live mutants. It should be noted that the proposed method eliminated only the affectless mutants. The key technical benefit of the suggested solution is that mutation of the instructions that don't propagate errors is avoided. These findings can lead to a performance improvement in the existing mutation-test methods and tools.
Açıklama
Anahtar Kelimeler
Software Test, Equivalent Mutant, Error Propagation, Machine Learning, Instruction Classification
Kaynak
Journal of Electronic Testing-Theory and Applications
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
39
Sayı
5-6
