Technical debt forecasting: An empirical study on open-source repositories

Tsoukalas, Dimitrios; Kehagias, Dionysios; Siavvas, Miltiadis; Chatzigeorgiou, Alexander

doi:10.1016/j.jss.2020.110777

Cited by 36 publications

(46 citation statements)

References 102 publications

(151 reference statements)

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“…The main goal of the SDK4ED project is to minimize cost, development time, and complexity of lowenergy software development processes, by providing a set of innovative solutions (i.e., toolboxes) integrated into the form of an easy-to-use platform for automatic optimization and trade-off calculation among important design-time and run-time software quality attributes. More specifically, the outcome of the project so far showcases numerous research and technical achievements with respect to the optimization of the targeted quality attributes, namely Maintainability [3,5,16,37,50], Dependability [32,33,[51][52][53][54], Energy Consumption [23,24,42,67], as well as Quality Forecasting [60,61,63] and Decision Support [47,[55][56][57] throughout the overall software development cycle. The SDK4ED TD Forecasting tool, integrated into the TD Management (TDM) framework, aims to provide predictive forecasts regarding the evolution of the TD quality attribute.…”

Section: Rq : Can Data Clustering Improve the Accuracy Of Crossproject Td Forecasting?mentioning

confidence: 99%

“…In our previous work [60,61], we have initially approached the TD forecasting challenge by employing statistical time series models, which have been widely used for predicting software evolution trends. In addition to statistical time series, we have also introduced and investigated other more sophisticated approaches that can be applied for TD forecasting, such as machine learning (ML) and causal models.…”

Section: Introductionmentioning

confidence: 99%

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A Clustering Approach Towards Cross-Project Technical Debt Forecasting

et al. 2021

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Technical debt (TD) describes quality compromises that can yield short-term benefits but may negatively affect the quality of software products in the long run. A wide range of tools and techniques have been introduced over the years in order for the developers to be able to determine and manage TD. However, being able to also predict its future evolution is of equal importance to avoid its accumulation, and, in turn, the unlikely event of making the project unmaintainable. Although recent research endeavors have showcased the feasibility of building accurate project-specific TD forecasting models, there is a gap in the field regarding cross-project TD forecasting. Cross-project TD forecasting is of practical importance, since it would enable the application of pre-existing forecasting models on previously unknown software projects, especially new projects that do not exhibit sufficient commit history to enable the construction of project-specific models. To this end, in the present paper, we focus on cross-project TD forecasting, and we examine whether the consideration of similarities between software projects could be the key for more accurate forecasting. More specifically, we propose an approach based on data clustering. In fact, a relatively large repository of software projects is divided into clusters of similar projects with respect to their TD aspects, and specific TD forecasting models are built for each cluster, using regression algorithms. According to our approach, previously unknown software projects are assigned to one of the defined clusters and the cluster-specific TD forecasting model is applied to predict future TD values. The approach was evaluated through several experiments based on real-world applications. The results of the analysis suggest that the proposed approach comprises a promising solution for accurate cross-project TD forecasting.

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Section: Rq : Can Data Clustering Improve the Accuracy Of Crossproject Td Forecasting?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

et al. 2021

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“…Hence, the Forecaster Toolbox provides three individual web services, namely TD Forecaster, Energy Forecaster, and Dependability Forecaser, which provide ML models for predicting the evolution of TD, Energy Consumption, and Dependability, respectively. Novel models have been developed for this purpose, especially for the case of TD forecasting [40,41,43].…”

Section: Technical Debt Management Toolboxmentioning

confidence: 99%

“…The last paper [42] proposes a clustering-based approach that aims to improve cross-project TD forecasting models. Tsoukalas et al highlight the issue of performing TD forecasting for new software projects lacking a reliable and sufficient number of past commits [41]. Thus, authors investigate whether the consideration of TD-related similarities could lead to more accurate cross-project forecasting.…”

Section: Special Issue Paper Highlightsmentioning

confidence: 99%

Investigating the Interaction between Energy Consumption, Quality of Service, Reliability, Security, and Maintainability of Computer Systems and Networks

et al. 2021

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“…The goal of SDK4ED is to minimize the cost and the development time of software development processes, by providing tools for automatic optimization of multiple quality requirements, such as technical debt, energy efficiency, dependability, and performance. The SDK4ED project so far has proposed numerous research and technical methods either with respect to the forecast and optimization of the targeted quality attributes (Energy, Maintainability, and Dependability) [4,7,18,21], or by investigating and studying the trade-offs between them [14,19,20]. The research work presented in this article is part of the energy consumption estimation component, which is integrated in the energy toolbox of the SDK4ED platform.…”

Section: Introductionmentioning

confidence: 99%

A Flexible Tool for Estimating Applications Performance and Energy Consumption Through Static Analysis

et al. 2021

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The design requirements of modern applications that target embedded systems, such as the need for high performance and low energy consumption, impose challenges on developers. Software tools capable of providing performance and energy consumption estimations are useful for addressing these challenges. Such tools aim to reduce development time and alleviate the time-to-market pressure. In this work, we propose a flexible tool that enables the estimation of performance and energy consumption of the application on embedded devices, providing a complete methodology based on which the user can add estimation models for various platforms. In contrast to existing tools that either rely on dynamic instrumentation or require detailed modeling of the hardware, the proposed tool leverages static analysis techniques applied at instruction level coupled with data-driven regression models. The proposed method is tested using a widely used benchmark suite for evaluation.

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Technical debt forecasting: An empirical study on open-source repositories

Cited by 36 publications

References 102 publications

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

Investigating the Interaction between Energy Consumption, Quality of Service, Reliability, Security, and Maintainability of Computer Systems and Networks

A Flexible Tool for Estimating Applications Performance and Energy Consumption Through Static Analysis

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