Researchers in the dynamic program analysis field have extensively used cluster analysis to address various problems. Typically, the clustering techniques are applied onto execution profiles having high dimensionality (i.e., involving a large number of profiling elements), sometimes in the order of thousands or even hundreds of thousands. Our concern is that the high number of profiling elements might diminish the effectiveness of the clustering process, which led us to explore the use of dimensionality reduction techniques as a preprocessing step to clustering. Specifically, in this work, we used PCA (Principal Component Analysis) as a dimensionality reduction technique and investigated its impact on two cluster-based analysis techniques, one aiming at identifying coincidentally correct tests, and the other at test suite minimization. In other words, we tried to assess whether PCA improves cluster-based analysis. Our experimental results showed that the impact was positive on the first technique, but inconclusive on the second, which calls for further investigation in the future.

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The interest in leveraging data mining and statistical techniques to enable dynamic program analysis has increased tremendously in recent years. Researchers have presented numerous techniques that mine and analyze execution profiles to assist software testing and other reliability enhancing approaches. Previous empirical studies have shown that the effectiveness of such techniques is likely to be impacted by the type of profiled program elements. This work further studies the impact of the characteristics of execution profiles by focusing on their size; noting that a typical profile comprises a large number of program elements, in the order of thousands or higher. Specifically, the authors devised six reduction techniques and comparatively evaluated them by measuring the following: (1) reduction rate; (2) information loss; (3) impact on two applications of dynamic program analysis, namely, cluster-based test suite minimization (App-I), and profilebased online failure and intrusion detection (App-II). The results were promising as the following: (a) the average reduction rate ranged from 92% to 98%; (b) three techniques were lossless and three were slightly lossy; (c) reducing execution profiles exhibited a major positive impact on the effectiveness and efficiency of App-I; and (d) reduction exhibited a positive impact on the efficiency of App-II, but a minor negative impact on its effectiveness. the transitivity relationships induced by control and data dependences [4], which suggests that high levels of reduction might be achieved.Hereafter, a profiled program element will also be referred to as program element for short; noting that in the context of this work, program elements represent covered statements, branches, def-uses, information flow pairs [5], slice pairs [5], paths [6], and possibly other program constructs that are also structural in nature.A well-established approach that the authors previously used [7] to reduce the high dimensionality and redundancy in execution profiles is PCA [8,9]. But PCA transforms the original data to a new coordinate system, which is problematic for some applications of dynamic program analysis because this might negatively impact the interpretability of learning models and the extraction of useful intrinsic properties.This work focuses on reduction techniques that preserve the original coordinate system to evade the limitation that PCA suffers from. In other words, the concern is with feature selection techniques [10] as opposed to feature extraction techniques [8]. Specifically, the authors will investigate different search strategies for feature selection based on the following: (1) an information theoretic approach [11], namely, the symmetric uncertainty measure (SU) (2) a heuristic randomized search approach, specifically the genetic algorithm (GA); and (3) a hybrid approach, which combines (1) and (2).The presented techniques are evaluated by measuring the following: (1) reduction rate;(2) information loss; and (3) impact on two applications of dynamic program analy...

Researchers have applied cluster analysis onto execution profiles induced by test cases in order to solve problems in the area of software testing and analysis. The employed clustering techniques varied, and no study was conducted to rate these techniques in terms of their effectiveness in this specific domain. This work aims at doing so experimentally. Specifically, given test sets comprising passing and failing test cases, we measure the performance of each technique at isolating the failing test cases from the passing cases. The study included one technique from each of the six main families of clustering algorithms. Our results suggested the following ranking of the evaluated techniques from best to worst: DBSCAN, K-Means, Agglomerative-AGNES and WaveCluster, Fuzzy-FCM, and K-Subspace.