Automated Software Engineering

, Volume 22, Issue 1, pp 111–141 | Cite as

SMPLearner: learning to predict software maintainability

Article

Abstract

Accurate and practical software maintainability prediction enables organizations to effectively manage their maintenance resources and guide maintenance-related decision making. This paper presents SMPLearner, an automated learning-based approach to train maintainability predictors by harvesting the actual average maintenance effort computed from the code change history as well as employing a much richer set of 44 four-level hierarchical code metrics collected by static code analysis tools. We systematically evaluated SMPLearner on 150 observations partitioned from releases of eight large scale open source software systems. Our evaluation showed that SMPLearner not only outperformed the traditional 4-metric MI model but also the recent learning-based maintainability predictors constructed based on single Class-level metrics, demonstrating that single Class-level metrics were not sufficient for maintainability prediction.

Keywords

Software Maintainability Maintenance effort Software metric  Machine learning 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSouthern Methodist UniversityDallasUSA
  2. 2.Human Language Technology Research InstituteUniversity of Texas at DallasRichardsonUSA
  3. 3.State Key Laboratory for Novel Software Technology, Software InstituteNanjing UniversityNanjingChina

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