Many software engineering activities process the events contained in log files. However, before performing any processing activity, it is necessary to parse the entries in a log file, to retrieve the actual events recorded in the log. Each event is denoted by a log message, which is composed of a fixed part—called (event) template—that is the same for all occurrences of the same event type, and a variable part, which may vary with each event occurrence. The formats of log messages, in complex and evolving systems, have numerous variations, are typically not entirely known, and change on a frequent basis; therefore, they need to be identified automatically. The log message format identification problem deals with the identification of the different templates used in the messages of a log. Any solution to this problem has to generate templates that meet two main goals: generating templates that are not too general, so as to distinguish different events, but also not too specific, so as not to consider different occurrences of the same event as following different templates; however, these goals are conflicting. In this paper, we present the approach approach, which recasts the log message identification problem as a multi-objective problem. approach uses an evolutionary approach to solve this problem, by tailoring the NSGA-II algorithm to search the space of solutions for a Pareto optimal set of message templates. We have implemented approach in a tool, which we have evaluated on six real-world datasets, containing log files with a number of entries ranging from 2K to 300K. The experiments results show that approach extracts by far the highest number of correct log message templates, significantly outperforming two state-of-the-art approaches on all datasets.@en

Regression testing is arguably one of the most important activities in software testing. However, its cost-effectiveness and usefulness can be largely impaired by complex system test cases that are poorly designed (e.g., test cases containing multiple test scenarios combined into a single test case) and that require a large amount of time and resources to run. One way to mitigate this issue is decomposing such system test cases into smaller, separate test cases - -each of them with only one test scenario and with its corresponding assertions - -so that the execution time of the decomposed test cases is lower than the original test cases, while the test effectiveness of the original test cases is preserved. This decomposition can be achieved with program slicing techniques, since test cases are software programs too. However, existing static and dynamic slicing techniques exhibit limitations when (1) the test cases use external resources, (2) code instrumentation is not a viable option, and (3) test execution is expensive. In this paper, we propose a novel approach, called DS3 (Decomposing System teSt caSe), which automatically decomposes a complex system test case into separate test case slices. The idea is to use test case execution logs, obtained from past regression testing sessions, to identify "hidden"dependencies in the slices generated by static slicing. Since logs include run-time information about the system under test, we can use them to extract access and usage of global resources and refine the slices generated by static slicing. We evaluated DS3 in terms of slicing effectiveness and compared it with a vanilla static slicing tool. We also compared the slices obtained by DS3 with the corresponding original system test cases, in terms of test efficiency and effectiveness. The evaluation results on one proprietary system and one open-source system show that DS3 is able to accurately identify the dependencies related to the usage of global resources, which vanilla static slicing misses. Moreover, the generated test case slices are, on average, 3.56 times faster than original system test cases and they exhibit no significant loss in terms of fault detection effectiveness.@en