Abstract
To gain a deeper empirical understanding of how developers work on Android apps, we investigate self-reported activities of Android developers and to what extent these activities can be classified with machine learning techniques. To this aim, we firstly create a taxonomy of self-reported activities coming from the manual analysis of 5,000 commit messages from 8,280 Android apps. Then, we study the frequency of each category of self-reported activities identified in the taxonomy, and investigate the feasibility of an automated classification approach. Our findings can inform be used by both practitioners and researchers to take informed decisions or support other software engineering activities.
Original language | English |
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Title of host publication | Proceedings - 2018 ACM/IEEE 5th International Conference on Mobile Software Engineering and Systems, MOBILESoft 2018 |
Place of Publication | New York, NY |
Publisher | ACM, IEEE Computer Society |
Pages | 144-155 |
Number of pages | 12 |
ISBN (Print) | 9781450357128 |
DOIs | |
Publication status | Published - 27 May 2018 |
Event | 5th ACM/IEEE 5th International Conference on Mobile Software Engineering and Systems, MOBILESoft 2018, collocated with the 40th International Conference on Software Engineering, ICSE 2018 - Gothenburg, Sweden Duration: 27 May 2018 → 28 May 2018 |
Conference
Conference | 5th ACM/IEEE 5th International Conference on Mobile Software Engineering and Systems, MOBILESoft 2018, collocated with the 40th International Conference on Software Engineering, ICSE 2018 |
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Country | Sweden |
City | Gothenburg |
Period | 27/05/18 → 28/05/18 |
Keywords
- Android
- empirical study
- mining software repositories