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Register a new userAutomated Identification of On-hold Self-admitted Technical Debt
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Added by
Rungroj Maipradit
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Modern software is developed under considerable time pressure, which implies that developers more often than not have to resort to compromises when it comes to code that is well written and code that just does the job. This has led over the past decades to the concept of technical debt, a short-term hack that potentially generates long-term maintenance problems. Self-admitted technical debt (SATD) is a particular form of technical debt: developers consciously perform the hack but also document it in the code by adding comments as a reminder (or as an admission of guilt). We focus on a specific type of SATD, namely On-hold SATD, in which developers document in their comments the need to halt an implementation task due to conditions outside of their scope of work (e.g., an open issue must be closed before a function can be implemented). We present an approach, based on regular expressions and machine learning, which is able to detect issues referenced in code comments, and to automatically classify the detected instances as either On-hold (the issue is referenced to indicate the need to wait for its resolution before completing a task), or as cross-reference, (the issue is referenced to document the code, for example to explain the rationale behind an implementation choice). Our approach also mines the issue tracker of the projects to check if the On-hold SATD instances are superfluous and can be removed (i.e., the referenced issue has been closed, but the SATD is still in the code). Our evaluation confirms that our approach can indeed identify relevant instances of On-hold SATD. We illustrate its usefulness by identifying superfluous On-hold SATD instances in open source projects as confirmed by the original developers.
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Self-Admitted Technical Debt (SATD) is a special form of technical debt in which developers intentionally record their hacks in the code by adding comments for attention. Here, we focus on issue-related On-hold SATD, where developers suspend proper implementation due to issues reported inside or outside the project. When the referenced issues are resolved, the On-hold SATD also need to be addressed, but since monitoring these issue reports takes a lot of time and effort, developers may not be aware of the resolved issues and leave the On-hold SATD in the code. In this paper, we propose FixMe, a GitHub bot that helps developers detecting and monitoring On-hold SATD in their repositories and notify them whenever the On-hold SATDs are ready to be fixed (i.e. the referenced issues are resolved). The bot can automatically detect On-hold SATD comments from source code using machine learning techniques and discover referenced issues. When the referenced issues are resolved, developers will be notified by FixMe bot. The evaluation conducted with 11 participants shows that our FixMe bot can support them in dealing with On-hold SATD. FixMe is available at https://www.fixmebot.app/ and FixMes VDO is at https://youtu.be/YSz9kFxN_YQ.
Technical debt occurs when software engineers favour short-term operability over long-term stability. Since this puts software stability at risk, technical debt requires early attention (failing which it accumulates interest). Most of existing work focus on detecting technical debts through code comment (i.e. self-admitted technical debt). However, there are many cases where technical debts are not explicitly acknowledged but deeply hidden in the code. In this paper, we propose a more comprehensive solution to deal with technical debt. We design a framework that caters for both cases of the existence of a comment. If a comment is absent and our framework detects a technical debt hidden in the code, it will automatically generate a relevant comment that can be attached with the code. We explore different implementations of this framework and the evaluation results demonstrate the applicability and effectiveness of our framework.
Self-Admitted Technical Debt (SATD) is a metaphorical concept to describe the self-documented addition of technical debt to a software project in the form of source code comments. SATD can linger in projects and degrade source-code quality, but it can also be more visible than unintentionally added or undocumented technical debt. Understanding the implications of adding SATD to a software project is important because developers can benefit from a better understanding of the quality trade-offs they are making. However, empirical studies, analyzing the survivability and removal of SATD comments, are challenged by potential code changes or SATD comment updates that may interfere with properly tracking their appearance, existence, and removal. In this paper, we propose SATDBailiff, a tool that uses an existing state-of-the-art SATD detection tool, to identify SATD in method comments, then properly track their lifespan. SATDBailiff is given as input links to open source projects, and its output is a list of all identified SATDs, and for each detected SATD, SATDBailiff reports all its associated changes, including any updates to its text, all the way to reporting its removal. The goal of SATDBailiff is to aid researchers and practitioners in better tracking SATDs instances and providing them with a reliable tool that can be easily extended. SATDBailiff was validated using a dataset of previously detected and manually validated SATD instances. SATDBailiff is publicly available as an open-source, along with the manual analysis of SATD instances associated with its validation, on the project website
Technical Debt is a metaphor used to describe the situation in which long-term code quality is traded for short-term goals in software projects. In recent years, the concept of self-admitted technical debt (SATD) was proposed, which focuses on debt that is intentionally introduced and described by developers. Although prior work has made important observations about admitted technical debt in source code, little is known about SATD in build systems. In this paper, we coin the term Self-Admitted Build Debt (SABD) and through a qualitative analysis of 500 SABD comments in the Maven build system of 300 projects, we characterize SABD by location and rationale (reason and purpose). Our results show that limitations in tools and libraries, and complexities of dependency management are the most frequent causes, accounting for 49% and 23% of the comments. We also find that developers often document SABD as issues to be fixed later. To automate the detection of SABD rationale, we train classifiers to label comments according to the surrounding document content. The classifier performance is promising, achieving an F1-score of 0.67-0.75. Finally, within 16 identified ready-to-be-addressed SABD instances, the three SABD submitted by pull requests and the five SABD submitted by issue reports were resolved after developers were made aware. Our work presents the first step towards understanding technical debt in build systems and opens up avenues for future work, such as tool support to track and manage SABD backlogs.
Context: Technical Debt requirements are related to the distance between the ideal value of the specification and the systems actual implementation, which are consequences of strategic decisions for immediate gains, or unintended changes in context. To ensure the evolution of the software, it is necessary to keep it managed. Identification and measurement are the first two stages of the management process; however, they are little explored in academic research in requirements engineering. Objective: We aimed at investigating which evidence helps to strengthen the process of TD requirements management, including identification and measurement. Method: We conducted a Systematic Literature Review through manual and automatic searches considering 7499 studies from 2010 to 2020, and including 61 primary studies. Results: We identified some causes related to Technical Debt requirements, existing strategies to help in the identification and measurement, and metrics to support the measurement stage. Conclusion: Studies on TD requirements are still preliminary, especially on management tools. Yet, not enough attention is given to interpersonal issues, which are difficulties encountered when performing such activities, and therefore also require research. Finally, the provision of metrics to help measure TD is part of this works contribution, providing insights into the application in the requirements context.
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