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Vulnerabilities need to be detected and removed from software. Although previous studies demonstrated the usefulness of employing prediction techniques in deciding about vulnerabilities of software components, the accuracy and improvement of effectiveness of these prediction techniques is still a grand challenging research question. This paper proposes a hybrid technique based on combining N-gram analysis and feature selection algorithms for predicting vulnerable software components where features are defined as continuous sequences of token in source code files, i.e., Java class file. Machine learning-based feature selection algorithms are then employed to reduce the feature and search space. We evaluated the proposed technique based on some Java Android applications, and the results demonstrated that the proposed technique could predict vulnerable classes, i.e., software components, with high precision, accuracy and recall.

The effectiveness of coverage-based fault localizations in the presence of multiple faults has been a major concern for the software testing research community. A commonly held belief is that the fault localization techniques based on coverage statistics are less effective in the presence of multiple faults and their performance deteriorates. The fault interference phenomenon refers to cases where the software under test contains multiple faults whose interactions hinder effective debugging. The immediate research question that arises is to what extent fault interactions are influential.This paper focuses on verifying the existence of fault interference phenomenon in programs developed in programming languages with object-oriented features. The paper then statistically measures the influence and significance of fault interactions on the performance of debugging based on coverage-based fault localizations. The result verifies that the fault interleaving phenomenon occurs. However, its impact on the performance of fault localizations is negligible.

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