The Impact of Instance Selection Algorithms On Maintenance Effort Estimation For Open-Source Software

Open-source software are very used nowadays in the industry, and the perfor-mance of the estimation of their maintenance effort becomes an interesting research topic. In this context, researchers have conducted many open-source soft-ware maintenance effort estimation (O-MEE) studies based on statistical and ma-chine learning (ML) techniques for better estimation. This study focuses on the impact of instance selection on the performance of ML techniques in O-MEE, mainly for bug resolution. An empirical study was conducted using three tech-niques: K-nearest neighbor (kNN), support vector machine (SVM), and multi-nomial naïve Bayes (MNB) using all-kNN instance selection algorithms on three datasets: Eclipse JDT, Eclipse Platform, and Mozilla Thunderbird datasets. This study reports on a set of 18 experiments and a comparison of the results. The results of this study show that instance selection helped make ML techniques more performant.