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Auflistung nach Schlagwort "Bug detection"

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  • Konferenzbeitrag
    Post-Debugging in Large Scale Big Data Analytic Systems
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Bergen, Eduard; Edlich, Stefan
    Data scientists often need to fine tune and resubmit their jobs when processing a large quantity of data in big clusters because of a failed behavior of currently executed jobs. Consequently, data scientists also need to filter, combine, and correlate large data sets. Hence, debugging a job locally helps data scientists to figure out the root cause and increases efficiency while simplifying the working process. Discovering the root cause of failures in distributed systems involve a different kind of information such as the operating system type, executed system applications, the execution state, and environment variables. In general, log files contain this type of information in a cryptic and large structure. Data scientists need to analyze all related log files to get more insights about the failure and this is cumbersome and slow. Another possibility is to use our reference architecture. We extract remote data and replay the extraction on the developer’s local debugging environment.
  • Konferenzbeitrag
    Variable Misuse Detection: Software Developers versus Neural Bug Detectors
    (Software Engineering 2023, 2023) Richter, Cedric; Haltermann, Jan; Jakobs, Marie-Christine; Pauck, Felix; Schott, Stefan; Wehrheim, Heike
    Finding and fixing software bugs is a central part of software development. Developers are therefore often confronted with the task of identifying whether a code snippet contains a bug and where it is located. Recently, data-driven approaches have been employed to automate this process. These so called neural bug detectors are trained on millions of buggy and correct code snippets to learn the task of bug detection. This raises the question how the performance of neural bug detectors and software developers compare. As a first step, we study this question in the context of variable misuse bugs. To this end, we performed a study with over 100 software developers and two state-of-the-art approaches for neural bug detection. Our study shows that software developers are on average slightly better than neural bug detectors – even though the bug detectors are trained specifically for this task. In addition, we identified several bottlenecks in existing neural bug detectors which could be mitigated in the future to improve their bug detection performance.