Technical Debt Management Automation: State of the Art and Future Perspectives

TL;DR

This paper provides a comprehensive overview of current automation approaches in technical debt management, analyzing tools, challenges, and future directions based on a systematic mapping study of 178 research articles.

Contribution

It offers the first systematic review of TDM automation, classifies existing artifacts, and proposes a conceptual model to synthesize current research and identify future challenges.

Findings

121 automation artifacts identified and classified

Research extensively covers various TDM activities automation

Integration of automation artifacts remains a key challenge

Abstract

Technical Debt (TD) refers to non-optimal decisions made in software projects that may lead to short-term benefits, but potentially harm the system's maintenance in the long-term. Technical debt management (TDM) refers to a set of activities that are performed to handle TD, e.g., identification. These activities can entail tasks such as code and architectural analysis, which can be time-consuming if done manually. Thus, substantial research work has focused on automating TDM tasks (e.g., automatic identification of code smells). However, there is a lack of studies that summarize current approaches in TDM automation. This can hinder practitioners in selecting optimal automation strategies to efficiently manage TD. It can also prevent researchers from understanding the research landscape and addressing the research problems that matter the most. Thus, the main objective of this study is to provide an overview of the state of the art in TDM automation, analyzing the available tools, their use, and the challenges in automating TDM. For this, we conducted a systematic mapping study (SMS), and from an initial set of 1086 primary studies, 178 were selected to answer three research questions covering different facets of TDM automation. We found 121 automation artifacts, which were classified in 4 different types (i.e., tools, plugins, scripts, and bots); the inputs/outputs and interfaces were also collected and reported. Finally, a conceptual model is proposed that synthesizes the results and allows to discuss the current state of TDM automation and related challenges. The results show that the research community has investigated to a large extent how to perform various TDM activities automatically, considering the number of studies and automation artifacts we identified. More research is needed towards fully automated TDM, specially concerning the integration of the automation artifacts.