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 MoLFI approach, which recasts the log message identification problem as a multi-objective problem. MoLFI 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 MoLFI 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 MoLFI extracts by far the highest number of correct log message templates, significantly outperforming two state-of-the-art approaches on all datasets. CCS CONCEPTS• Software and its engineering → Search-based software engineering;

We present a fuzzing framework for Intents: the core IPC mechanism for intra-and inter-app communication in Android. Since intents lie at a trust boundary between apps, their correctness is important and thorough testing is warranted. The key challenge is to balance the tension between generating intents that applications expect, permitting deep penetration into application logic, and generating intents that trigger interesting bugs that have not been previously uncovered. Our work strikes this balance using a novel combination of static analysis and random test-case generation. Our intent fuzzer crashed dozens of Google core and top Google Play apps, resulting in app restarts or even in a complete OS reboot.

Complex interactions and the distributed nature of wireless sensor networks make automated testing and debugging before deployment a necessity. A main challenge is to detect bugs that occur due to non-deterministic events, such as node reboots or packet duplicates. Often, these events have the potential to drive a sensor network and its applications into corner-case situations, exhibiting bugs that are hard to detect using existing testing and debugging techniques.In this paper, we present KleeNet, a debugging environment that effectively discovers such bugs before deployment. KleeNet executes unmodified sensor network applications on symbolic input and automatically injects non-deterministic failures. As a result, KleeNet generates distributed execution paths at high-coverage, including low-probability cornercase situations. As a case study, we integrated KleeNet into the Contiki OS and show its effectiveness by detecting four insidious bugs in the µIP TCP/IP protocol stack. One of these bugs is critical and lead to refusal of further connections.

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We present KleeNet, a Klee based bug hunting tool for sensor network applications before deployment. KleeNet automatically tests code for all possible inputs, ensures memory safety, and integrates well into TinyOS based application development life cycle, making it easy for developers to test their applications.