Automatically Detecting Developer Activities and Problems in Software Development Work.
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| Title: | Automatically Detecting Developer Activities and Problems in Software Development Work. |
|---|---|
| Authors: | Roehm, Tobias1 roehm@cs.tum.edu, Maalej, Walid1 maalejw@cs.tum.edu |
| Source: | ICSE: International Conference on Software Engineering. Feb2012, p1261-1264. 4p. |
| Subjects: | Computer software development, Computer programming management, Computer software developers, Hidden Markov models, Machine learning |
| Abstract: | Detecting the current activity of developers and problems they are facing is a prerequisite for a context-aware assistance and for capturing developers' experiences during their work. We present an approach to detect the current activity of software developers and if they are facing a problem. By observing developer actions like changing code or searching the web, we detect whether developers are locating the cause of a problem, searching for a solution, or applying a solution. We model development work as recurring problem solution cycle, detect developer's actions by instrumenting the IDE, translate developer actions to observations using ontologies, and infer developer activities by using Hidden Markov Models. In a preliminary evaluation, our approach was able to correctly detect 72% of all activities. However, a broader more reliable evaluation is still needed. [ABSTRACT FROM AUTHOR] |
| Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatically Detecting Developer Activities and Problems in Software Development Work. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Roehm%2C+Tobias%22">Roehm, Tobias</searchLink><relatesTo>1</relatesTo><i> roehm@cs.tum.edu</i><br /><searchLink fieldCode="AR" term="%22Maalej%2C+Walid%22">Maalej, Walid</searchLink><relatesTo>1</relatesTo><i> maalejw@cs.tum.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. Feb2012, p1261-1264. 4p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming+management%22">Computer programming management</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+developers%22">Computer software developers</searchLink><br /><searchLink fieldCode="DE" term="%22Hidden+Markov+models%22">Hidden Markov models</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Detecting the current activity of developers and problems they are facing is a prerequisite for a context-aware assistance and for capturing developers' experiences during their work. We present an approach to detect the current activity of software developers and if they are facing a problem. By observing developer actions like changing code or searching the web, we detect whether developers are locating the cause of a problem, searching for a solution, or applying a solution. We model development work as recurring problem solution cycle, detect developer's actions by instrumenting the IDE, translate developer actions to observations using ontologies, and infer developer activities by using Hidden Markov Models. In a preliminary evaluation, our approach was able to correctly detect 72% of all activities. However, a broader more reliable evaluation is still needed. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=78198195 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 4 StartPage: 1261 Subjects: – SubjectFull: Computer software development Type: general – SubjectFull: Computer programming management Type: general – SubjectFull: Computer software developers Type: general – SubjectFull: Hidden Markov models Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Automatically Detecting Developer Activities and Problems in Software Development Work. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Roehm, Tobias – PersonEntity: Name: NameFull: Maalej, Walid IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2012 Type: published Y: 2012 Titles: – TitleFull: ICSE: International Conference on Software Engineering Type: main |
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