Software and systems traceability is essential for downstream tasks such as data-driven software analysis and intelligent tool development. However, despite the increasing attention to mining and understanding technical debt in software systems, specific tools for supporting the track of technical debts are rarely available. In this work, we propose the first programming language-independent tracking tool for self-admitted technical debt (SATD) – a sub-optimal solution that is explicitly annotated by developers in software systems. Our approach takes a git repository as input and returns a list of SATDs with their evolution actions (created, deleted, updated) at the commit-level. Our approach also returns a line number indicating the latest starting position of the corresponding SATD in the system. Our SATD tracking approach first identifies an initial set of raw SATDs (which only have created and deleted actions) by detecting and tracking SATDs in commits’ hunks, leveraging a state-of-the-art language-independent SATD detection approach. Then it calculates a context-based matching score between pairs of deleted and created raw SATDs in the same commits to identify SATD update actions. The results of our preliminary study on Apache Tomcat and Apache Ant show that our tracking tool can achieve a F1 score of 92.8% and 96.7% respectively.

Self-admitted technical debt (SATD) refers to technical debt that is intentionally introduced by developers and explicitly documented in code comments or other software artifacts (e.g., issue reports) to annotate sub-optimal decisions made by developers in the software development process.

In this article, we propose a new encoding scheme for named entity recognition (NER) called Joined Type-Length encoding (JoinedTL). Unlike most existing named entity encoding schemes, which focus on flat entities, JoinedTL can label nested named entities in a single sequence. JoinedTL uses a packed encoding to represent both type and span of a named entity, which not only results in less tagged tokens compared to existing encoding schemes, but also enables it to support nested NER. We evaluate the effectiveness of JoinedTL for nested NER on three nested NER datasets: GENIA in English, GermEval in German, and PerNest, our newly created nested NER dataset in Persian. We apply CharLSTM+WordLSTM+CRF, a three-layer sequence tagging model on three datasets encoded using JoinedTL and two existing nested NE encoding schemes, i.e., JoinedBIO and JoinedBILOU. Our experiment results show that CharLSTM+WordLSTM+CRF trained with JoinedTL encoded datasets can achieve competitive F1 scores as the ones trained with datasets encoded by two other encodings, but with 27%–48% less tagged tokens. To leverage the power of three different encodings, i.e., JoinedTL, JoinedBIO, and JoinedBILOU, we propose an encoding-based ensemble method for nested NER. Evaluation results show that the ensemble method achieves higher F1 scores on all datasets than the three models each trained using one of the three encodings. By using nested NE encodings including JoinedTL with CharLSTM+WordLSTM+CRF, we establish new state-of-the-art performance with an F1 score of 83.7 on PerNest, 74.9 on GENIA, and 70.5 on GermEval, surpassing two recent neural models specially designed for nested NER.