Reducing execution profiles: techniques and benefits

SummaryThe 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 profile‐based 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. Copyright © 2014 John Wiley & Sons, Ltd. This work studied the impact of the size of execution profiles. It presented several reduction techniques and comparatively evaluated them by measuring the reduction rate, information loss, and impact on two software analysis techniques. The results were promising as the average reduction rate ranged from 92% to 98%, most techniques were lossless or slightly lossy, and reducing execution profiles was shown to benefit software analysis in terms of efficiency and/or effectiveness.