Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings - SEKE 2019 |
| Subtitle of host publication | 31st International Conference on Software Engineering and Knowledge Engineering |
| Pages | 94-101 |
| Number of pages | 8 |
| ISBN (electronic) | 1891706489 |
| Publication status | Published - 1 Jan 2019 |
| Event | 31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019 - Lisbon, Portugal Duration: 10 Jul 2019 → 12 Jul 2019 |
Publication series
| Name | Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE |
|---|---|
| Volume | 2019-July |
| ISSN (Print) | 2325-9000 |
| ISSN (electronic) | 2325-9086 |
Abstract
In agile software development, the sprint performances and dynamics of teams often imply tendencies for the success of a project. Post mortem strategies, e.g., retrospectives help the team to report and share individually gained experiences (positives and negatives) from previous sprints, and enable them to use these experiences for future sprint planning. The interpretation of effects on sprint performance is often subjective, especially with concern to social-driven factors in teams. Involving strategies from predictive analytics in sprint retrospectives could reduce potential interpretation gaps of dynamics, and enhance the pre-knowledge, also awareness situation when preparing for the next sprint. In a case study involving 15 software projects with a total of 130 involved undergraduate students, we investigated the post-effects on team performances and behavioral-driven factors when providing predictive analytics in retrospectives. Besides measures for productivity, we consider human factors, e.g., team structures, communication, meetings and mood affects in teams as well as project success metrics. We developed a unique JIRA plugin called ProDynamics that collects performance information from projects and derives trend-insights for next sprints. The ProDynamics plugin enables the use of a times series and neural network model within a JIRA system to interpret factorial dependencies and behavioral pattern, thus to show the next sprint course of a team.
Keywords
- Agile, Data analytics, Futurespectives, Human factors, Sprint performances, Team dynamics
ASJC Scopus subject areas
- Computer Science(all)
- Software
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Proceedings - SEKE 2019: 31st International Conference on Software Engineering and Knowledge Engineering. 2019. p. 94-101 (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE; Vol. 2019-July).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Sprint performance forecasts in agile software development
T2 - 31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019
AU - Kortum, Fabian
AU - Klünder, Jil
AU - Brunotte, Wasja
AU - Schneider, Kurt
N1 - Funding information: This work was funded by the German Research Society (DFG) under the project name Team Dynamics (2018-2020). Grant number 263807701. ACKNOWLEDGMENT This work was funded by the German Research Society (DFG) under the project name Team Dynamics (2018-2020). Grant number 263807701.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In agile software development, the sprint performances and dynamics of teams often imply tendencies for the success of a project. Post mortem strategies, e.g., retrospectives help the team to report and share individually gained experiences (positives and negatives) from previous sprints, and enable them to use these experiences for future sprint planning. The interpretation of effects on sprint performance is often subjective, especially with concern to social-driven factors in teams. Involving strategies from predictive analytics in sprint retrospectives could reduce potential interpretation gaps of dynamics, and enhance the pre-knowledge, also awareness situation when preparing for the next sprint. In a case study involving 15 software projects with a total of 130 involved undergraduate students, we investigated the post-effects on team performances and behavioral-driven factors when providing predictive analytics in retrospectives. Besides measures for productivity, we consider human factors, e.g., team structures, communication, meetings and mood affects in teams as well as project success metrics. We developed a unique JIRA plugin called ProDynamics that collects performance information from projects and derives trend-insights for next sprints. The ProDynamics plugin enables the use of a times series and neural network model within a JIRA system to interpret factorial dependencies and behavioral pattern, thus to show the next sprint course of a team.
AB - In agile software development, the sprint performances and dynamics of teams often imply tendencies for the success of a project. Post mortem strategies, e.g., retrospectives help the team to report and share individually gained experiences (positives and negatives) from previous sprints, and enable them to use these experiences for future sprint planning. The interpretation of effects on sprint performance is often subjective, especially with concern to social-driven factors in teams. Involving strategies from predictive analytics in sprint retrospectives could reduce potential interpretation gaps of dynamics, and enhance the pre-knowledge, also awareness situation when preparing for the next sprint. In a case study involving 15 software projects with a total of 130 involved undergraduate students, we investigated the post-effects on team performances and behavioral-driven factors when providing predictive analytics in retrospectives. Besides measures for productivity, we consider human factors, e.g., team structures, communication, meetings and mood affects in teams as well as project success metrics. We developed a unique JIRA plugin called ProDynamics that collects performance information from projects and derives trend-insights for next sprints. The ProDynamics plugin enables the use of a times series and neural network model within a JIRA system to interpret factorial dependencies and behavioral pattern, thus to show the next sprint course of a team.
KW - Agile
KW - Data analytics
KW - Futurespectives
KW - Human factors
KW - Sprint performances
KW - Team dynamics
UR - http://www.scopus.com/inward/record.url?scp=85071383105&partnerID=8YFLogxK
U2 - 10.18293/seke2019-224
DO - 10.18293/seke2019-224
M3 - Conference contribution
AN - SCOPUS:85071383105
T3 - Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
SP - 94
EP - 101
BT - Proceedings - SEKE 2019
Y2 - 10 July 2019 through 12 July 2019
ER -