An Evaluation of Selection Methods for Time-Aware Effort Estimation
| Autor: | Chris Lokan, Sousuke Amasaki |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Estimation
Generality Computer science business.industry 020207 software engineering Potential method Context (language use) 02 engineering and technology Machine learning computer.software_genre Variety (cybernetics) Data modeling 020204 information systems 0202 electrical engineering electronic engineering information engineering Selection method Artificial intelligence Project management business computer |
| Zdroj: | APSEC |
| DOI: | 10.1109/apsec.2017.105 |
| Popis: | CONTEXT: Several studies in effort estimation havefound that it can be effective to use only recent project data for building an effort estimation model. The generality of this timeaware approach has been explored across a variety of effort estimation model approaches, organizations and definitions of recency. However, other studies have shown that it is not alwayshelpful. A question arises: how can one tell whether the approachwould be effective for a given target project? OBJECTIVE: Toinvestigate a potential method to decide between selecting recentor all project data. METHOD: Using a single-company ISBSGdata set1 studied previously in similar research, we propose andevaluate a selection method. The method utilizes a variant ofcross-validation based on recent projects to make the decision.RESULTS: There are significant differences in the estimation accuracybetween using the proposed method and using the growingportfolio (always using all available data). The method could alsoselect the better approach on average. However, the differencein estimation accuracy between using the proposed method andalways using moving windows was not statistically significant.CONCLUSIONS: The selection method could select the betterapproach on average. The results contribute to developing amethod for suggesting a better approach for practitioners. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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