The impact of software metrics in NASA metric data program dataset modules for software defect prediction

Adinda Ayu Puspita Ramadhani (Lambung Mangkurat University)
Radityo Adi Nugroho (Lambung Mangkurat University)
Mohammad Reza Faisal (Lambung Mangkurat University)
Friska Abadi (Lambung Mangkurat University)
Rudy Herteno (Lambung Mangkurat University)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 22, No 4: August 2024

This paper discusses software metrics and their impact on software defect prediction values in the NASA metric data program (MDP) dataset. The NASA MDP dataset consists of four categories of software metrics: halstead, McCabe, LoC, and misc. However, there is no study showing which metrics participate in increasing the area under the curve (AUC) value of the NASA MDP dataset. This study utilizes 12 modules from the NASA MDP dataset, where these 12 modules are being tested into 14 relationships of software metrics derived from the four existing metric categories. Subsequently, classification is performed using the k-nearest neighbor (kNN) method. The research concludes that software metrics have a significant impact on the AUC value, with the LoC+McCabe+misc metrics relationship influencing the improvement of the AUC value. However, the metrics relationship that has the most impact on achieving less optimal AUC values is McCabe. Halstead metric also plays a role in decreasing the performance of other metrics.