Sztwiertnia, Sebastian ; Grübel, Maximilian ; Chouchane, Amine ; Sokolowski, Daniel ; Narasimhan, Krishna ; Mezini, Mira
Hrsg.: Wang, Shuai ; Xie, Xiaofei ; Ma, Lei (2021)
Impact of programming languages on machine learning bugs.
30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA '21). virtual Conference (12.07.2021)
doi: 10.1145/3464968.3468408
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Machine learning (ML) is on the rise to be ubiquitous in modern software. Still, its use is challenging for software developers. So far, research has focused on the ML libraries to find and mitigate these challenges. However, there is initial evidence that programming languages also add to the challenges, identifiable in different distributions of bugs in ML programs. To fill this research gap, we propose the first empirical study on the impact of programming languages on bugs in ML programs. We plan to analyze software from GitHub and related discussions in GitHub issues and Stack Overflow for bug distributions in ML programs, aiming to identify correlations with the chosen programming language, its features and the application domain. This study's results enable better-targeted use of available programming language technology in ML programs, preventing bugs, reducing errors and speeding up development.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Herausgeber: | Wang, Shuai ; Xie, Xiaofei ; Ma, Lei |
| Autor(en): | Sztwiertnia, Sebastian ; Grübel, Maximilian ; Chouchane, Amine ; Sokolowski, Daniel ; Narasimhan, Krishna ; Mezini, Mira |
| Art des Eintrags: | Bibliographie |
| Titel: | Impact of programming languages on machine learning bugs |
| Sprache: | Englisch |
| Publikationsjahr: | 11 Juli 2021 |
| Verlag: | ACM |
| Buchtitel: | AISTA 2021: Proceedings of the 1st ACM International Workshop on AI and Software Testing/Analysis |
| Veranstaltungstitel: | 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA '21) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 12.07.2021 |
| DOI: | 10.1145/3464968.3468408 |
| Kurzbeschreibung (Abstract): | Machine learning (ML) is on the rise to be ubiquitous in modern software. Still, its use is challenging for software developers. So far, research has focused on the ML libraries to find and mitigate these challenges. However, there is initial evidence that programming languages also add to the challenges, identifiable in different distributions of bugs in ML programs. To fill this research gap, we propose the first empirical study on the impact of programming languages on bugs in ML programs. We plan to analyze software from GitHub and related discussions in GitHub issues and Stack Overflow for bug distributions in ML programs, aiming to identify correlations with the chosen programming language, its features and the application domain. This study's results enable better-targeted use of available programming language technology in ML programs, preventing bugs, reducing errors and speeding up development. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Softwaretechnik |
| Hinterlegungsdatum: | 01 Mär 2024 13:31 |
| Letzte Änderung: | 04 Jun 2024 08:27 |
| PPN: | 518808769 |
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| Suche nach Titel in: | TUfind oder in Google |
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