| Texto completo | |
| Autor(es): |
Ribeiro, Henrique L.
;
de Araujo, Roberto P. A.
;
Chaim, Marcos L.
;
de Souza, Higor A.
;
Kon, Fabio
;
IEEE
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2019 13TH ACM/IEEE INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT (ESEM 2019); v. N/A, p. 11-pg., 2019-01-01. |
| Resumo | |
Background: Debugging is a key task during the software development cycle. Spectrum-based Fault Localization (SFL) is a promising technique to improve and automate debugging. SFL techniques use control-flow spectra to pinpoint the most suspicious program elements. However, data-flow spectra provide more detailed information about the program execution, which may be useful for fault localization. Aims: We evaluate the effectiveness and efficiency of ten SFL ranking metrics using data-flow spectra. Method: We compare the performance of data- and control-flow spectra for SFL using 163 faults from 5 real-world open source programs, which contain from 468 to 4130 test cases. The data- and control-flow spectra types used in our evaluation are definition-use associations (DUAs) and lines, respectively. Results: Using data-flow spectra, up to 50% more faults are ranked in the top-15 positions compared to control-flow spectra. Also, most SFL ranking metrics present better effectiveness using data-flow to inspect up to the top-40 positions. The execution cost of data-flow spectra is higher than control-flow, taking from 22 seconds to less than 9 minutes. Dataflow has an average overhead of 353% for all programs, while the average overhead for control-flow is of 102%. Conclusions: The results suggest that SFL techniques can benefit from using data-flow spectra to classify faults in better positions, which may lead developers to inspect less code to find bugs. The execution cost to gather data-flow is higher compared to control-flow, but it is not prohibitive. Moreover, data-flow spectra also provide information about suspicious variables for fault localization, which may improve the developers' performance using SFL. (AU) | |
| Processo FAPESP: | 14/50937-1 - INCT 2014: da Internet do Futuro |
| Beneficiário: | Fabio Kon |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 14/23030-5 - Avaliação do uso prático de técnica de localização de defeitos |
| Beneficiário: | Higor Amario de Souza |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Doutorado |
| Processo FAPESP: | 13/24992-2 - Localização de Defeitos em Depuração Automatizada |
| Beneficiário: | Higor Amario de Souza |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |