Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Better bug reporting with better privacy
Proceedings of the 13th international conference on Architectural support for programming languages and operating systems
ReCrash: Making Software Failures Reproducible by Preserving Object States
ECOOP '08 Proceedings of the 22nd European conference on Object-Oriented Programming
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Execution synthesis: a technique for automated software debugging
Proceedings of the 5th European conference on Computer systems
It Does Matter How You Normalise the Branch Distance in Search Based Software Testing
ICST '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification and Validation
Striking a new balance between program instrumentation and debugging time
Proceedings of the sixth conference on Computer systems
Camouflage: automated anonymization of field data
Proceedings of the 33rd International Conference on Software Engineering
On parameter tuning in search based software engineering
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
BugRedux: reproducing field failures for in-house debugging
Proceedings of the 34th International Conference on Software Engineering
| Hi-index | 0.00 |
The task of debugging software failures is generally time consuming and involves substantial manual effort. A crucial part of this task lies in the reproduction of the reported failure at the developer's site. In this paper, we propose a novel framework that aims to address the problem of failure reproduction by employing an adaptive search-based approach in combination with a limited amount of instrumentation. In particular, we formulate the problem of reproducing failures as a search problem: reproducing a software failure can be viewed as the search for a set of inputs that lead its execution to the failing path. The search is guided by information obtained through instrumentation. Preliminary experiments on small-size programs show promising results in which the proposed approach outperforms random search.