Localizing State-Dependent Faults Using Associated Sequence Mining

In this thesis we developed a new fault localization process to localize faults in object oriented software. The process is built upon the Encapsulation\u27\u27 principle and aims to locate state-dependent discrepancies in the software\u27s behavior. We experimented with the proposed process on 50 seeded faults in 8 subject programs, and were able to locate the faulty class in 100% of the cases when objects with constant states were taken into consideration, while we missed 24% percent of the faults when these objects were not considered. We also developed a customized data mining technique Associated sequence mining\u27\u27 to be used in the localization process; experiments showed that it only provided slight enhancement to the result of the process. The customization provided at least 17% enhancement in the time performance and it is generic enough to be applicable in other domains. In addition to that we have developed an extensive taxonomy for object-oriented software faults based on UML models. We used the taxonomy to make decisions regarding the localization process. It provides an aid for understanding the nature of software faults, and will help enhance the different tasks related to software quality assurance. The main contributions of the thesis were based on preliminary experimentation on the usability of the classification algorithms implemented in WEKA in software fault localization, which resulted in the conclusion that both the fault type and the mechanism implemented in the analysis algorithm were significant to affect the results of the localization

Classification of Software Behaviors for Failure Detection: A Discriminative Pattern Mining Approach

Software is a ubiquitous component of our daily life. We often depend on the correct working of software systems. Due to the difficulty and complexity of software systems, bugs and anomalies are prevalent. Bugs have caused billions of dollars loss, in addition to privacy and security threats. In this work, we address software reliability issues by proposing a novel method to classify software behaviors based on past history or runs. With the technique, it is possible to generalize past known errors and mistakes to capture failures and anomalies. Our technique first mines a set of discriminative features capturing repetitive series of events from program execution traces. It then performs feature selection to select the best features for classification. These features are then used to train a classifier to detect failures. Experiments and case studies on traces of several benchmark software systems and a real-life concurrency bug from MySQL server show the utility of the technique in capturing failures and anomalies. On average, our pattern-based classification technique outperforms the baseline approach by 24.68 % in accuracy 1