Identifying non-natural language artifacts in bug reports

Autor: Hirsch, Thomas, Hofer, Birgit

Bug reports are a popular target for natural language processing (NLP). However, bug reports often contain artifacts such as code snippets, log outputs and stack traces. These artifacts not only inflate the bug reports with noise, but often constitut

Externí odkaz: http://arxiv.org/abs/2110.01336

Root cause prediction based on bug reports

Autor: Hirsch, Thomas, Hofer, Birgit

Publikováno v: Proceedings of the 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), Coimbra, Portugal, 2020, pp. 171-176

This paper proposes a supervised machine learning approach for predicting the root cause of a given bug report. Knowing the root cause of a bug can help developers in the debugging process - either directly or indirectly by choosing proper tool suppo

Externí odkaz: http://arxiv.org/abs/2103.02372

On the Refinement of Spreadsheet Smells by means of Structure Information

Autor: Koch, Patrick, Hofer, Birgit, Wotawa, Franz

Spreadsheet users are often unaware of the risks imposed by poorly designed spreadsheets. One way to assess spreadsheet quality is to detect smells which attempt to identify parts of spreadsheets that are hard to comprehend or maintain and which are

Externí odkaz: http://arxiv.org/abs/1810.04542

Combining Spreadsheet Smells for Improved Fault Prediction

Autor: Koch, Patrick, Schekotihin, Konstantin, Jannach, Dietmar, Hofer, Birgit, Wotawa, Franz

Spreadsheets are commonly used in organizations as a programming tool for business-related calculations and decision making. Since faults in spreadsheets can have severe business impacts, a number of approaches from general software engineering have

Externí odkaz: http://arxiv.org/abs/1805.10493

Using textual bug reports to predict the fault category of software bugs

Autor: Hirsch, Thomas, Hofer, Birgit

Publikováno v: In Array September 2022 15