Software technical debt prediction based on complex software networks
Bo Jiang, Jiaye Cen, Erluan Zhu, Jiale Wang
TL;DR
This paper introduces a new way to predict software technical debt by combining network analysis metrics with traditional metrics, improving model performance.
Contribution
The novel contribution is combining Social Network Analysis metrics with traditional TD metrics to enhance TDP model performance.
Findings
The combined metric suite improves the performance of technical debt prediction models.
XGBoost classifier achieves the best performance with high recall and F2 score.
Different metric combinations show varying effectiveness in TDP.
Abstract
Technical debt prediction (TDP) is crucial for the long-term maintainability of software. In the literature, many machine-learning based TDP models have been proposed; they used TD-related metrics as input features for machine-learning classifiers to build TDP models. However, their performance is unsatisfactory. Developing and utilizing more effective metrics to build TDP models is considered as a promising approach to enhance the performance of TDP models. Social Network Analysis (SNA) uses a set of metrics (i.e., SNA metrics) to characterize software elements (classes, binaries, etc.) in software from the perspective of software as a whole. SNA metrics are regarded as a compensation of TD-related metrics used in the existing TDP work, and thus are expected to improve the performance of existing TDP models. However, the effectiveness of SNA metrics in the field of TDP has never been…
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Taxonomy
TopicsSoftware Engineering Research · Software Reliability and Analysis Research · Software System Performance and Reliability