Predicting and quantifying the technical debt in cloud software engineering

Skourletopoulos, Georgios; Mavromoustakis, Constandinos X.; Bahsoon, Rami; Mastorakis, George; Pallis, Evangelos

doi:10.1109/camad.2014.7033201

Cited by 28 publications

(8 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, as a software system evolves, so does the TD that accompanies it. While several researchers have studied the evolution of TD and its implications for the software development process [2,3,12,16,17,59], the small amount of existing research works on TD evolution forecasting [58,60,61] highlights the gap in the field.…”

Section: Td Forecastingmentioning

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]. In another study by Tsoukalas et al [60], the authors use time series analysis to model and forecast TD evolution.…”

Section: Td Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Td Forecastingmentioning

confidence: 99%

Section: Td Forecastingmentioning

confidence: 99%

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

et al. 2021

View full text Add to dashboard Cite

show abstract

“…However, although many studies have focused on the evolution of software systems, only a few have focused on the evolution of TD [59]. In fact, to the best of our knowledge the only known study on TD forecasting is [60], in which, Scourletopoulos et al attempt to introduce the concept of predicting TD for Software as a Service (SaaS) systems, by exploiting COCOMO, a software cost model proposed by Boehm [61]. However, their study is limited only to cloud computing systems.…”

Section: Software Evolution and Technical Debt Forecastingmentioning

confidence: 99%

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

Tsoukalas

Siavvas

Janković

et al. 2018

2018 International Conference on Intelligent Systems (IS)

View full text Add to dashboard Cite

Technical debt (TD), a metaphor inspired by the financial debt of economic theory, indicates quality compromises that can yield short-term benefits in the software development process, but may negatively affect the long-term quality of software products. Numerous techniques, methods, and tools have been proposed over the years for estimating and managing TD, providing a variety of options to the developers and project managers of software applications. However, apart from managing TD, predicting its future value is equally important since this knowledge is expected to facilitate decision-making tasks regarding software implementation and maintenance, such as incurring or paying off TD instances. To this end, the purpose of the present study is to (i) summarize the work that has been conducted until today in the field of TD estimation and forecasting, and (ii) to identify existing open issues that have not been adequately addressed yet and require further research. The present survey led to two interesting observations. Firstly, none of the existing TD estimation methods and tools has reached a desired level of maturity, while a large volume of previously uninvestigated metrics and techniques exist that could potentially increase the completeness of TD estimation. Secondly, no notable contributions exist in the field of TD forecasting, indicating that it is a scarcely investigated field. The latter constitutes the main finding of the present literature review, since TD forecasting could lead to the development of practical decision-making mechanisms, which could assist developers and project managers in taking proactive actions regarding TD repayment.

show abstract

“…Quality of experience is a novel feature that aims to measure how the users relate to an application; it is best quantified by the ability to receive service and the waiting time for the service to complete [34]. Quality of experience or QoE is critical for providers as it could affect their revenue.…”

Section: Related Workmentioning

confidence: 99%

Enabling Mobile Cloud Wide Spread Through an Evolutionary Market-Based Approach

Chilipirea

Petre

Dobre

et al. 2016

IEEE Systems Journal

View full text Add to dashboard Cite

Mobile clouds are an ongoing research topic that has yet to become ubiquitous as the now popular cloud paradigm. This is because of a number of issues with mobile clouds that still need to be addressed such as: incentives, security, privacy, context, data management, usability, and cost benefits. Out of these issues, the most important one that needs to be addressed is the issue of incentives, without which mobile clouds cannot gain enough users for the concept to be useful. Unlike public, company-owned cloud systems, in mobile clouds, the amount of resources or processing power is directly dependent on mobile cloud users that are in the proximity of the individual that requires extra resources. With an increase in the number of mobile cloud users willing to share resources or willing to use the service offered by others, comes an increase in the likeliness that enough mobile-cloud-enabled devices will be available. In this paper, we study incentives for mobile cloud systems and consider as a solution an evolutionary market-based approach to create these incentives. Creating a market for these systems is particularly difficult because of the large number of individuals that need to be involved and their high mobility.

show abstract

Predicting and quantifying the technical debt in cloud software engineering

Cited by 28 publications

References 22 publications

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

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

Enabling Mobile Cloud Wide Spread Through an Evolutionary Market-Based Approach

Contact Info

Product

Resources

About