Abstract Context Previous research on software aging is limited, with a focus on dynamic runtime indicators like memory and performance, often neglecting evolutionary indicators like source code comments and narrowly examining legacy issues within the Technical Debt (TD) context. Objective We introduce the concept of Aging Debt (AD), representing the increased maintenance efforts and costs needed to keep software updated. We study AD through Self-Admitted Aging Debt (SAAD) observed in source code comments left by software developers. Method We employ a mixed-methods approach, combining qualitative and quantitative analyses to detect and measure AD in software. This includes framing SAAD patterns from the source code comments after analysing the source code context, then utilizing the SAAD patterns to detect SAAD comments. In the process, we develop a taxonomy for SAAD that reflects the temporal aging of software and its associated debt. Then we utilize the taxonomy to quantify the different types of AD prevalent in Open Source Software (OSS) repositories. Results Our proposed taxonomy categorizes evolutionary software aging into Active and Dormant types. Our extensive analysis of over 9,000+ OSS repositories reveals that more than 21% repositories exhibit signs of SAAD as observed from our gold standard SAAD dataset. Notably, Dormant AD emerges as the predominant category, highlighting a critical but often overlooked aspect of software maintenance. Conclusion As software volume grows annually, so do evolutionary aging and maintenance challenges; our proposed taxonomy can aid researchers in detailed software aging studies and help practitioners develop improved and proactive maintenance strategies.

Most Self-Admitted Technical Debt (SATD) research utilizes explicit SATD features such as ‘TODO’ and ‘FIXME’ for SATD detection. A closer look reveals several SATD research uses simple SATD (‘Easy to Find’) code comments without contextual data (preceding and succeeding source code context). This work addresses this gap through PENTACET (or 5C dataset) data. PENTACET is a large Curated Contextual Code Comments per Contributor and the most extensive SATD data. We mine 9,096 Open Source Software Java projects totaling over 400 million LOC. The outcome is a dataset with 23 million code comments, preceding and succeeding source code context for each comment, and more than 250,000 SATD comments, including both ‘Easy to Find’ and ‘Hard to Find’ SATD. We believe PENTACET data will further SATD research using Artificial Intelligence techniques.

Numerous tools exist for mining source code and software development process metrics. However, very few publicly available tools focus on source code comments, a crucial software artifact. This paper presents SoCCMiner (Source Code-Comments and Comment-Context Miner), a tool that offers multiple mining pipelines. It is the first readily available (plug-and-play) and customizable open-source tool for mining source code contextual information of comments at different granularities (Class comments, Method comments, Interface comments, and other granular comments). Mining comments at different source code granularities can aid researchers and practitioners working in a host of applications that focus on source code comments, such as Self-Admitted Technical Debt, Program Comprehension, and other applications. Furthermore, SoCCMiner is highly adaptable and extendable to include additional attributes and support other programming languages. This prototype supports the Java programming language.

A high imbalance exists between technical debt and non-technical debt source code comments. Such imbalance affects Self-Admitted Technical Debt (SATD) detection performance, and existing literature lacks empirical evidence on the choice of balancing technique. In this work, we evaluate the impact of multiple balancing techniques, including Data level, Classifier level, and Hybrid, for SATD detection in Within-Project and Cross-Project setup. Our results show that the Data level balancing technique SMOTE or Classifier level Ensemble approaches Random Forest or XGBoost are reasonable choices depending on whether the goal is to maximize Precision, Recall, F1, or AUC-ROC. We compared our best-performing model with the previous SATD detection benchmark (cost-sensitive Convolution Neural Network). Interestingly the top-performing XGBoost with SMOTE sampling improved the Within-project F1 score by 10% but fell short in Cross-Project set up by 9%. This supports the higher generalization capability of deep learning in Cross-Project SATD detection, yet while working within individual projects, classical machine learning algorithms can deliver better performance. We also evaluate and quantify the impact of duplicate source code comments in SATD detection performance. Finally, we employ SHAP and discuss the interpreted SATD features. We have included the replication package1and shared a web-based SATD prediction tool2with the balancing techniques in this study.