Methods and Tools for TD Estimation and Forecasting: A State-of-the-art Survey

Tsoukalas, Dimitrios; Siavvas, Miltiadis; Janković, Marija; Kehagias, Dionysios; Chatzigeorgiou, Alexander; Tzovaras, Dimitrios

doi:10.1109/is.2018.8710521

Cited by 18 publications

(17 citation statements)

References 54 publications

(74 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%

“…The significance of developing forecasting methods for accurate prediction of TD evolution as well as their potential impact while integrated into accompanying tools has been brought to light by a related study [63], in which the authors state that it would be interesting to examine if such methods could result in the development of higher quality software products. In a first study towards addressing this challenge, a study by Skourletopoulos et al [58] proposes a method for predicting the TD of Software as a Service (SaaS) applications using the COCOMO software cost model [7].…”

Section: Td Forecastingmentioning

confidence: 99%

“…TD indicators are indicators usually related to software metrics [1,40] that allow researchers and practitioners to point out TD-related quality issues residing in various attributes, features, or characteristics of a software artefact. Recent literature has identified a variety of software metrics that can act as potential TD indicators, while there is also a multitude of assessment tools [63] that support TD estimation based on these metrics. For instance, metrics such as cyclomatic complexity, code duplication or low test coverage (i.e.…”

Section: Datasetmentioning

confidence: 99%

“…On the other hand, predicting the future TD during the software evolution is a new emerging field of study that can be considered equally important, as the software system and its TD emerge in parallel [17]. TD forecasting could give project managers and developers the opportunity to timely react on the accumulation of TD by employing appropriate repayment activities (e.g., code refactoring) promptly [63] and thus, maintain a software application to a satisfactory quality level and avoid the unlikely event of reaching a point at which the software project becomes no longer maintainable. Whereas various researchers have addressed the topic of forecasting the evolution of various aspects directly or indirectly related to the TD of a software project, such as code smells [20], fault-proneness [4] and evolution trends [10], not too many concrete approaches have been proposed so far regarding the forecasting of TD itself.…”

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: Td Forecastingmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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%

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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A practical approach for technical debt prioritization based on class‐level forecasting

Tsoukalas

Siavvas

Kehagias

et al. 2023

J Software Evolu Process

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Monitoring technical debt (TD) is considered highly important for software companies, as it provides valuable information on the effort required to repay TD and in turn maintain the system. When it comes to TD repayment, however, developers are often overwhelmed with a large volume of TD liabilities that they need to fix, rendering the procedure effort demanding. Hence, prioritizing TD liabilities is of utmost importance for effective TD repayment. Existing approaches rely on the current TD state of the system; however, prioritization would be more efficient by also considering its future evolution. To this end, the present work proposes a practical approach for prioritization of TD liabilities by incorporating information retrieved from TD forecasting techniques, emphasizing on the class‐level granularity to provide highly actionable results. Specifically, the proposed approach considers the change proneness and forecasted TD evolution of software artifacts and combines it with proper visualization techniques, to enable the early identification of classes that are more likely to become unmaintainable. To demonstrate and evaluate the approach, an empirical study is conducted on six real‐world applications. The proposed approach is expected to facilitate developers better plan refactoring activities, in order to manage TD promptly and avoid unforeseen situations long term.

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Methods and Tools for TD Estimation and Forecasting: A State-of-the-art Survey

Cited by 18 publications

References 54 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 practical approach for technical debt prioritization based on class‐level forecasting

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