Name
Abstract | ||
|---|---|---|
Estimating the effort of software engineering tasks is notoriously hard but essential for project planning. The agile community often adopts issue reports to describe tasks, and story points to estimate task effort. In this paper, we propose a machine learning classifier for estimating the story points required to address an issue. Through empirical evaluation on one industrial project and eight open source projects, we demonstrate that such classifier is feasible. We show that ---after an initial training on over 300 issue reports--- the classifier estimates a new issue in less than 15 seconds with a mean magnitude of relative error between 0.16 and 0.61. In addition, issue type, summary, description, and related components prove to be project dependent features pivotal for story point estimation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2972958.2972959 | PROMISE |
Field | DocType | Citations |
Point estimation,Data mining,Computer science,Agile software development,Project planning,Artificial intelligence,Classifier (linguistics),Machine learning,Approximation error,Learning classifier system | Conference | 5 |
PageRank | References | Authors |
0.46 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Porru | 1 | 31 | 4.88 |
| Alessandro Murgia | 2 | 246 | 16.20 |
| Serge Demeyer | 3 | 2250 | 291.74 |
| Michele Marchesi | 4 | 807 | 120.28 |
| R. Tonelli | 5 | 237 | 18.42 |