Name
Abstract | ||
|---|---|---|
In a modern software system, when a program fails, a crash report which contains an execution trace would be sent to the software vendor for diagnosis. A crash report which corresponds to a failure could be caused by multiple types of faults simultaneously. Many large companies such as Baidu organize a team to analyze these failures, and classify them into multiple labels (i.e., multiple types of faults). However, it would be time-consuming and difficult for developers to manually analyze these failures and come out with appropriate fault labels. In this paper, we automatically classify a failure into multiple types of faults, using a composite algorithm named MLL-GA, which combines various multi-label learning algorithms by leveraging genetic algorithm (GA). To evaluate the effectiveness of MLL-GA, we perform experiments on 6 open source programs and show that MLL-GA could achieve average F-measures of 0.6078 to 0.8665. We also compare our algorithm with Ml.KNN and show that on average across the 6 datasets, MLL-GA improves the average F-measure of MI.KNN by 14.43%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CSMR-WCRE.2014.6747163 | CSMR-WCRE |
Keywords | Field | DocType |
public domain software,software vendor,genetic algorithm,mll-ga,multi-label learning,learning (artificial intelligence),f-measures,modern software system,execution trace,software maintenance,fault labels,multilabel software behavior learning,software behavior learning,crash report,baidu,genetic algorithms,software fault tolerance,ml.knn,open source programs,learning artificial intelligence | Crash,Data mining,Software behavior,Computer science,Software vendor,Software fault tolerance,Software system,Software,Artificial intelligence,Software maintenance,Machine learning,Genetic algorithm | Conference |
Citations | PageRank | References |
24 | 0.71 | 23 |
Authors | ||
5 | ||