| Title | A Possibilistic Evolutionary Approach to Handle the Uncertainty of Software Metrics Thresholds in Code Smells Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Boutaib, Sofien, Elarbi, Maha, Bechikh, Slim, Palomba, Fabio, Said, Lamjed Ben |
| Conference Name | 2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS) |
| Keywords | Code smells detection, codes, Computing Theory, Evolutionary algorithm, Measurement, Metrics, possibilistic K-NN, possibility distribution, pubcrawl, security metrics, Sociology, Software algorithms, software metrics, software quality, uncertain metrics' thresholds, Uncertainty |
| Abstract | A code smells detection rule is a combination of metrics with their corresponding crisp thresholds and labels. The goal of this paper is to deal with metrics' thresholds uncertainty; as usually such thresholds could not be exactly determined to judge the smelliness of a particular software class. To deal with this issue, we first propose to encode each metric value into a binary possibility distribution with respect to a threshold computed from a discretization technique; using the Possibilistic C-means classifier. Then, we propose ADIPOK-UMT as an evolutionary algorithm that evolves a population of PK-NN classifiers for the detection of smells under thresholds' uncertainty. The experimental results reveal that the possibility distribution-based encoding allows the implicit weighting of software metrics (features) with respect to their computed discretization thresholds. Moreover, ADIPOK-UMT is shown to outperform four relevant state-of-art approaches on a set of commonly adopted benchmark software systems. |
| DOI | 10.1109/QRS54544.2021.00068 |
| Citation Key | boutaib_possibilistic_2021 |
A Possibilistic Evolutionary Approach to Handle the Uncertainty of Software Metrics Thresholds in Code Smells Detection