Abstract-We present MUSE (MUtation-baSEd fault localization technique), a new fault localization technique based on mutation analysis. A key idea of MUSE is to identify a faulty statement by utilizing different characteristics of two groups of mutants-one that mutates a faulty statement and the other that mutates a correct statement. We also propose a new evaluation metric for fault localization techniques based on information theory, called Locality Information Loss (LIL): it can measure the aptitude of a localization technique for automated fault repair systems as well as human debuggers. The empirical evaluation using 14 faulty versions of the five real-world programs shows that MUSE localizes a fault after reviewing 7.4 statements on average, which is about 25 times more precise than the state-of-the-art SBFL technique Op2.

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Abstract-Test suite augmentation techniques are used in regression testing to identify code elements affected by changes and to generate test cases to cover those elements. In previous work, we studied two approaches to augmentation, one using a concolic test case generation algorithm and one using a genetic test case generation algorithm. We found that these two approaches behaved quite differently in terms More recently, we empirically studied several factors that can affect augmentation techniques [34]. These include (1) the order in which affected elements are considered while generating test cases, (2) the manner in which existing and newly generated test cases are used, and (3) the algorithm used to generate test cases. The results of our studies show that the primary factor affecting augmentation is the test case

Although testing is a standard method for improving the quality of software, conventional testing methods often fail to detect faults. Concolic testing attempts to remedy this by automatically generating test cases to explore execution paths in a program under test, helping testers achieve greater coverage of program behavior in a more automated fashion. Concolic testing, however, consumes a significant amount of computing time to explore execution paths, which is an obstacle toward its practical application. To address this limitation, we have developed a scalable distributed concolic testing framework that utilizes large numbers of computing nodes to generate test cases in a scalable manner. In this paper, we present the results of an empirical study that shows that the proposed framework can achieve a several orders-of-magnitude increase in test case generation speed compared to the original concolic approach, and also demonstrates clear potential for scalability.

Finding the root cause of a bug requires a significant effort from developers. Automated fault localization techniques seek to reduce this cost by computing the suspiciousness scores (i.e., the likelihood of program entities being faulty). Existing techniques have been developed by utilizing input features of specific types for the computation of suspiciousness scores, such as program spectrum or mutation analysis results. This article presents a novel learn-to-rank fault localization technique called PRecise machINe-learning-based fault loCalization tEchnique (PRINCE). PRINCE uses genetic programming (GP) to combine multiple sets of localization input features that have been studied separately until now. For dynamic features, PRINCE encompasses both Spectrum Based Fault Localization (SBFL) and Mutation Based Fault Localization (MBFL) techniques. It also uses static features, such as dependency information and structural complexity of program entities. All such information is used by GP to train a ranking model for fault localization. The empirical evaluation on 65 real-world faults from CoREBench, 84 artificial faults from SIR, and 310 real-world faults from Defects4J shows that PRINCE outperforms the state-of-the-art SBFL, MBFL, and learn-to-rank techniques significantly. PRINCE localizes a fault after reviewing 2.4% of the executed statements on average (4.2 and 3.0 times more precise than the best of the compared SBFL and MBFL techniques, respectively). Also, PRINCE ranks 52.9% of the target faults within the top ten suspicious statements.

Current industrial testing practices often build test cases in a manual manner, which is slow and ineffective. To alleviate this problem, concolic testing generates test cases that can achieve high coverage in an automated fashion. However, due to a large number of possible execution paths, concolic testing might not detect bugs even after spending significant amount of time. Thus, it is necessary to check if concolic testing can detect bugs in embedded software in a practical manner through case studies. This paper describes case studies of applying the concolic testing tool CREST to embedded C applications. Through this project, we have detected new faults in the Samsung Linux Platform (SLP) file manager, Samsung security library, and busybox ls.

Abstract. In today's information society, flash memory has become a virtually indispensable component, particularly for mobile devices. In order for mobile devices to operate successfully, it is essential that flash memory be controlled correctly through file system software. However, as is typical for embedded software, conventional testing methods often fail to detect hidden flaws in the software due to the difficulty of creating effective test cases. As a different approach, model checking techniques guarantee a complete analysis, but only on a limited scale. In this paper, we describe an empirical study wherein a concolic testing method is applied to the multi-sector read operation for a flash memory. This method combines a symbolic static analysis and a concrete dynamic analysis to automatically generate test cases and perform exhaustive path testing accordingly. In addition, we analyze the advantages and weaknesses of the concolic testing approach on the domain of the flash file system compared to model checking techniques.