Automatically Detecting Developer Activities and Problems in Software Development Work.

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]