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Behavior Model-Based Fault Localization for RESTful Web Applications
Jong-In Jang, Nakwon Lee, Duksan Ryu, Jongmoon Baik
http://doi.org/10.5626/JOK.2020.47.11.1044
Because of the nature of Web applications being more complex, larger in scale and more likely to be composed of black box components compared to traditional software systems wherein fault localization techniques are actively used, existing techniques can be only minimally applied to localize faults in Web applications. Also, existing studies to localize a fault in a complex system such as a Web application system also have limitations in capturing the indirect interactions in Web applications and suffers from the Web application’s dynamic nature. In this study, we propose a behavior modeling-based fault localization for the RESTful Web applications. The approach models a RESTful Web application as a sequence of behaviors that captures the direct and indirect interactions in the application. The modeling process is lightweight and it is not necessary to build the model in advance of the actual execution of application. The spectrum-based fault localization is then performed in the granularity of behavior pairs in the behavior model. To demonstrate the approach, a case study on the RESTful Web application built upon the YouTube Data API v3 was conducted and demonstrated that the approach can successfully resolve aforementioned difficulties and localize a fault in the RESTful Web application.
Improving Mutation-Based Fault Localization for Better Locating Omission Faults Using Coverage Change Information
http://doi.org/10.5626/JOK.2020.47.9.863
Although omission faults are bugs commonly found in real-world programs, existing mutation-based fault localization techniques show low accuracy at locating omission faults because useful mutants are not likely generated at locations where necessary statements are missed. This paper introduces two new techniques, MUSEUM+ and Metallaxis+, an extension of two mutationbased fault localization techniques, MUSEUM and Metallaxis, by adding elements that link the change of coverage information and the change of test results. The proposed MBFL techniques additionally utilize coverage change information to consider the characteristics of omission faults. The experiment with the 16 JFreeChart faults in Defects4J, including 10 omission faults and 6 non-omission faults demonstrate that the presented techniques, MUSEUM+ and Metallaxis+, show improved faults localization accuracy.
Analysis of Utilization Methods of the Statistical Model Checking Results for Localizing Faults on System of Systems
Sangwon Hyun, Yong-jun Shin, Doo-Hwan Bae
http://doi.org/10.5626/JOK.2020.47.4.380
System of Systems (SoS) is a large and complex system comprising independent constituent systems. Statistical Model Checking (SMC) techniques can be used to verify if the SoS achieves its goals or not. However, even if the SoS goal failure is detected using the SMC, finding a root cause of the SoS failure requires more cost than that of a system. One of the candidate solutions for reducing the debugging cost is to apply fault localization techniques on the SoS. However, existing fault localization techniques are designed to utilize testing results of a system. Thus, a method to utilize SMC results is needed to apply existing fault localization techniques to the SoS. In this study, we suggest six utilization methods of SMC results for SoS fault localization, and compare the performance of them on the emergency-response SoS. We found that the method based on the expectation value showed the best performance in the experiment.
Fault Localization Method by Utilizing Memory Update Information and Memory Partitioning based on Memory Map
Kwanhyo Kim, Ki-Yong Choi, Jung-Won Lee
In recent years, the cost of automotive ECU (Electronic Control Unit) has accounted for more than 30% of total car production cost. However, the complexity of testing and debugging an automotive ECU is increasing because automobile manufacturers outsource automotive ECU production. Therefore, a large amount of cost and time are spent to localize faults during testing an automotive ECU. In order to solve these problems, we propose a fault localization method in memory for developers who run the integration testing of automotive ECU. In this method, memory is partitioned by utilizing memory map, and fault-suspiciousness for each partition is calculated by utilizing memory update information. Then, the fault-suspicious region for partitions is decided based on calculated fault-suspiciousness. The preliminary result indicated that the proposed method reduced the fault-suspicious region to 15.01(%) of memory size.
Test Case Grouping and Filtering for Better Performance of Spectrum-based Fault Localization
Spectrum-based fault localization (SFL) method assigns a suspicious ratio. The statement is strongly affected by a failed test case compared to a passed test case. A failed test case assigns a suspicious ratio while a passed test case reduces some parts of assigned suspicious ratio. In the absence of a failed test case, it is impossible to localize the fault. Thus, a failed test case is very important for fault localization. However, spectrum-based fault localization has difficulty in reflecting the unique characteristics of a failed test because a failed test case and a passed test case are input at the same time to calculate a suspicious ratio. This paper supplements for this limitation and suggests a test case grouping method for more accurate fault localization. In addition, this paper suggested a filtering method considering test efficiency and verified the effectiveness by applying 65 algorithms. In 90 % of whole methods, the accuracy was improved by 13% and the effectiveness was improved by 72% based on EXAM score.
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