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 |
|---|---|
| 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 |
|---|---|
| Country | Sweden |
| City | Gothenburg |
| Period | 27/05/18 → 28/05/18 |
Fingerprint
Keywords
- Android
- empirical study
- mining software repositories
Cite this
}
Self-reported activities of Android developers. / Pascarella, Luca; Geiger, Franz Xaver; Palomba, Fabio; Di Nucci, Dario; Malavolta, Ivano; Bacchelli, Alberto.
Proceedings - 2018 ACM/IEEE 5th International Conference on Mobile Software Engineering and Systems, MOBILESoft 2018. New York, NY : ACM, IEEE Computer Society, 2018. p. 144-155.Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Self-reported activities of Android developers
AU - Pascarella, Luca
AU - Geiger, Franz Xaver
AU - Palomba, Fabio
AU - Di Nucci, Dario
AU - Malavolta, Ivano
AU - Bacchelli, Alberto
PY - 2018/5/27
Y1 - 2018/5/27
N2 - 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.
AB - 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.
KW - Android
KW - empirical study
KW - mining software repositories
UR - http://www.scopus.com/inward/record.url?scp=85051682506&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051682506&partnerID=8YFLogxK
U2 - 10.1145/3197231.3197251
DO - 10.1145/3197231.3197251
M3 - Conference contribution
SN - 9781450357128
SP - 144
EP - 155
BT - Proceedings - 2018 ACM/IEEE 5th International Conference on Mobile Software Engineering and Systems, MOBILESoft 2018
PB - ACM, IEEE Computer Society
CY - New York, NY
ER -