Name
Abstract | ||
|---|---|---|
Iterations are time-boxed periods with an intended outcome that is often a set of implemented requirements. Iterations are part of most common software development lifecycle models. Planning of iterations is a non-trivial task due to the multi-dimensional criteria. (1) The first dimension concerns the question what shall be completed in the iteration, also referred to as "release plan- ning". Decisions in this dimension are based on criteria such as dependencies and priorities of requirements. (2) The second di- mension concerns the decision, which project participant should work on which task, also referred to as "task assignment". Deci- sions in this dimension are based on criteria such as the expertise and the workload of the developers. The decisions in both dimen- sions are considerably complex. Therefore several approaches exist to semi-automatically support the decisions limited to one of the two dimensions mentioned above. None of the existing ap- proaches considers both dimensions at the same time. In this pa- per we propose a combination of approaches from semi-automatic release planning and from semi-automatic task assignment. This results in a semi-automated two-dimensional solution for the problem of iteration planning, We suggest the use of a genetic algorithm to optimize the resulting iteration plans in both dimen- sions of the problem. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1639950.1640065 | Conference on Object-Oriented Programming Systems, Languages, and Applications |
Keywords | Field | DocType |
release planning,semi-automatic release planning,dimension concern,genetic algorithm,resulting iteration plan,unified model,semi- automated,unicase,existing approach,common software development lifecycle,machine learning,semi-automatic task assignment,towards automation,iteration planning,non-trivial task,task assignment,two dimensions,software development | Computer science,Workload,Operations research,Theoretical computer science,Automation,Software development process,Management science,Genetic algorithm | Conference |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonas Helming | 1 | 123 | 9.03 |
| Maximilian Koegel | 2 | 142 | 10.22 |
| Zardosht Hodaie | 3 | 7 | 2.65 |