Technical Debt (TD) is a metaphor reflecting technical compromises that can yield short-term benefits but might hurt the long-term health of a software system. With the increasing amount of data generated when performing software development activities, an emergent research field has gained attention: applying Intelligent Techniques to solve Software Engineering problems. Intelligent Techniques were used to explore data for knowledge discovery, reasoning, learning, planning, perception, or supporting decision-making. Although these techniques can be promising, there is no structured understanding related to their application to support Technical Debt Management (TDM) activities. Within this context, this study aims to investigate to what extent the literature has proposed and evaluated solutions based on Intelligent Techniques to support TDM activities. To this end, we performed a Systematic Mapping Study (SMS) to investigate to what extent the literature has proposed and evaluated solutions based on Intelligent Techniques to support TDM activities. In total, 150 primary studies were identified and analyzed, dated from 2012 to 2021. The results indicated a growing interest in applying Intelligent Techniques to support TDM activities, the most used: Machine Learning and Reasoning under uncertainty. Intelligent Techniques aimed to assist mainly TDM activities related to identification, measurement, and monitoring. Design TD, Code TD, and Architectural TD are the TD types in the spotlight. Most studies were categorized at automation levels 1 and 2, meaning that existing approaches still require substantial human intervention. Symbolists and Analogizers are levels of explanation presented by most Intelligent Techniques, implying that these solutions conclude a general truth after considering a sufficient number of particular cases. Moreover, we also cataloged the empirical research types, contributions, and validation strategies described in primary studies. Based on our findings, we argue that there is still room to improve the use of Intelligent Techniques to support TDM activities. The open issues that emerged from this study can represent future opportunities for practitioners and researchers.

Technical Debt (TD) refers to the consequences of taking shortcuts when developing software. Technical Debt Management (TDM) becomes complex since it relies on a decision process based on multiple and heterogeneous data, which are not straightforward to be synthesized. In this context, there is a promising opportunity to use Intelligent Techniques to support TDM activities since these techniques explore data for knowledge discovery, reasoning, learning, or supporting decision-making. Although these techniques can be used for improving TDM activities, there is no empirical study exploring this research area. This study aims to identify and analyze solutions based on Intelligent Techniques employed to support TDM activities. A Systematic Mapping Study was performed, covering publications between 2010 and 2020. From 2276 extracted studies, we selected 111 unique studies. We found a positive trend in applying Intelligent Techniques to support TDM activities, being Machine Learning, Reasoning Under Uncertainty, and Natural Language Processing the most recurrent ones. Identification, measurement, and monitoring were the more recurrent TDM activities, whereas Design, Code, and Architectural were the most frequently investigated TD types. Although the research area is up-and-coming, it is still in its infancy, and this study provides a baseline for future research.

Technical debt is the metaphor for shortcuts in software development that bring short-term benefits, but long-term consequences hinder the process of maintaining and developing software. It is important to manage these technical debt items, as not all of them need to be paid. Having a list of prioritized debts is an essential step in decision-making in the management process. This work aims at finding technical debt prioritization methods, providing a classification of them. That is, methods to identify whether and when a technical debt should be paid off. We performed a systematic mapping review to find and analyze the main papers of the area, covering the main bases. We selected 112 studies, resulting in 51 unique papers. We classified the methods in a two-level taxonomy containing 10 categories according to their different possible outcomes. In addition, we have identified three methods results: boolean, category and ordered list. Finally, we have also identified practical technical characteristics and requirements for a method to prioritize technical debt items in real projects. Although several methods have been found in literature, none of them are adaptive to the context and are language-independent, nor cover several technical debt types. Moreover, there is a clear lack of tools to use them. So, in conclusion, the research on technical debt prioritization is still wide open. From this study, a combination of the techniques used in these methods can be tested and automated to assist in the decision-making process on which debts should be paid.

Technical Debt is a metaphor that has, in recent years, helped developers to think about and to monitor software quality. The metaphor refers to flaws in software (usually caused by shortcuts to save time) that may affect future maintenance and evolution. We conducted an empirical study in an academic environment, with nine teams of graduate and undergraduate students during two offerings of a laboratory course on Extreme Programming (XP Lab). The teams had a comprehensive lecture about several alternative ways to identify and manage Technical Debt. We monitored the teams, performed interviews, did close observations and collected feedback. The results show that the awareness of Technical Debt influences team behavior. Team members report thinking and discussing more about software quality after becoming aware of Technical Debt in their projects.

The Technical Debt (TD) metaphor has been used as a way to manage and communicate long-term consequences that some decisions may cause. However the state of the art in TD has not culminated yet in rigorous analysis models for large-scale projects. This work analyses an industrial project, from the perspective of its decisions and related events, so that we can better characterize the existence of TD and show the evolution of its parameters. The project in study had a life cycle of six years (2005-2011) and its data for analysis was collected from emails, documents, CVS logs, code files and interviews with developers and project managers. From this analysis, we identified the factors that had influence on the project decisions and their impact on the system along the time. Furthermore, we were able to extract a set of lessons associated with the characterization of TD in projects of this port.

The technical debt metaphor is increasingly being used to describe the effect of delaying certain software maintenance tasks on software projects. Practitioners understand intuitively how technical debt can turn into a serious problem if it is left unattended. However, it remains unknown how serious the problem is and whether explicit measurement and management of technical debt is useful. In this paper, we explore the effect of technical debt by tracking a single delayed maintenance task in a real software project throughout its lifecycle and simulate how explicit technical debt management might have changed project outcomes. The results from this study demonstrate how and to what extent technical debt affects software projects. The study also sheds light on the research methodologies that can be used to investigate the technical debt management problem.