Name
Abstract | ||
|---|---|---|
Automated debugging techniques aim to help developers locate and understand the cause of a failure, an extremely challenging yet fundamental task. Most state-of-the-art approaches suffer from two problems: they require a large number of passing and failing tests and report possible faulty code with no explanation. To mitigate these issues, we present MIMIC, a novel automated debugging technique that combines and extends our previous input generation and anomaly detection techniques. MIMIC (1) synthesizes multiple passing and failing executions similar to an observed failure and (2) uses these executions to detect anomalies in behavior that may explain the failure. We evaluated MIMIC on six failures of real-world programs with promising results: for five of these failures, MIMIC identified their root causes while producing a limited number of false positives. Most importantly, the anomalies identified by MIMIC provided information that may help developers understand (and ultimately eliminate) such root causes. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2642937.2643014 | ASE |
Keywords | Field | DocType |
anomaly detection,debugging,execution synthesis,testing and debugging | Anomaly detection,Data mining,Computer science,Theoretical computer science,Artificial intelligence,Machine learning,Debugging,False positive paradox | Conference |
Citations | PageRank | References |
11 | 0.51 | 25 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniele Zuddas | 1 | 19 | 2.05 |
| Wei Jin | 2 | 211 | 8.26 |
| Fabrizio Pastore | 3 | 329 | 23.60 |
| Leonardo Mariani | 4 | 233 | 24.60 |
| Alessandro Orso | 5 | 3550 | 172.85 |