Name
Abstract | ||
|---|---|---|
Software change prediction is crucial in order to efficiently plan resource allocation during testing and maintenance phases of a software. Moreover, correct identification of change-prone classes in the early phases of software development life cycle helps in developing cost-effective, good quality and maintainable software. An effective software change prediction model should equally recognize change-prone and not change-prone classes with high accuracy. However, this is not the case as software practitioners often have to deal with imbalanced data sets where instances of one type of class is much higher than the other type. In such a scenario, the minority classes are not predicted with much accuracy leading to strategic losses. This study evaluates a number of techniques for handling imbalanced data sets using various data sampling methods and MetaCost learners on six open-source data sets. The results of the study advocate the use of resample with replacement sampling method for effective imbalanced learning. |
Year | DOI | Venue |
|---|---|---|
2017 | https://doi.org/10.1007/s10664-016-9488-7 | Empirical Software Engineering |
Keywords | Field | DocType |
Change proneness,Data sampling,Empirical validation,Imbalanced learning,MetaCost learners,Object-oriented metrics | Data mining,Data set,Computer science,Resource allocation,Software,Sampling (statistics),Artificial intelligence,Systems development life cycle,Data sampling,Change prediction,Machine learning,Empirical research | Journal |
Volume | Issue | ISSN |
22 | 6 | 1382-3256 |
Citations | PageRank | References |
8 | 0.48 | 53 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruchika Malhotra | 1 | 533 | 35.12 |
| Megha Khanna | 2 | 59 | 6.47 |