Software Evolution Prediction Using Seasonal Time Analysis: A Comparative Study

Goulão, Miguel; Fonte, Nelson Baptista da; Wermelinger, Michel; Abreu, Fernando Brito e

doi:10.1109/csmr.2012.30

Cited by 27 publications

(22 citation statements)

References 22 publications

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“…The economics field has inspired researchers to apply economic estimation models employing autoregression to make evolutionary estimations on software evolution [16,17]. These studies have been focusing on defect prediction and are generally looking at short term estimations.…”

Section: Methodsmentioning

confidence: 99%

Automatic Means of Identifying Evolutionary Events in Software Development

Karus

2013

2013 IEEE International Conference on Software Maintenance

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The software development process patterns in open source software projects are not well known. Consequently, the longevity of new open source software projects is left up to subjective experiences of the development team. In this study, we are investigating a data mining approach for identifying relevant patterns in software development process. We demonstrate the capabilities of wavelet analysis on 27 open source software projects for identifying similar evolutionary patterns or events in different projects. The analysis identified close to 1000 evolutionary patterns common to multiple projects. The analysis of some of the patterns shows that the end of source code evolution of a project is determined early in the project. In addition, strong fluctuations of activity in sequential periods are identified as good indicators of problems in projects. In conclusion, the analysis reveals that wavelet analysis can be a powerful and objective tool for identifying evolutionary events that can be used as estimation basis or management guide in software projects.

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Section: Methodsmentioning

confidence: 99%

Automatic Means of Identifying Evolutionary Events in Software Development

Karus

2013

2013 IEEE International Conference on Software Maintenance

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“…and Radlinski applied association rule mining and Bayesian networks, respectively. Based on mathematical models, Auto‐Regressive Integrated Moving Average models , Cox survival analysis , and Weibull distribution are used. Kim et al .…”

Section: Challenges: Improving the Management Of Change Requestmentioning

confidence: 99%

Challenges and opportunities for software change request repositories: a systematic mapping study

Cavalcanti

Neto

Machado

et al. 2013

J Software Evolu Process

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Software maintenance starts as soon as the first artifacts are delivered and is essential for the success of the software. However, keeping maintenance activities and their related artifacts on track comes at a high cost. In this respect, change request (CR) repositories are fundamental in software maintenance. They facilitate the management of CRs and are also the central point to coordinate activities and communication among stakeholders. However, the benefits of CR repositories do not come without issues, and commonly occurring ones should be dealt with, such as the following: duplicate CRs, the large number of CRs to assign, or poorly described CRs. Such issues have led researchers to an increased interest in investigating CR repositories, by considering different aspects of software development and CR management. In this paper, we performed a systematic mapping study to characterize this research field. We analyzed 142 studies, which we classified in two ways. First, we classified the studies into different topics and grouped them into two dimensions: challenges and opportunities. Second, the challenge topics were classified in accordance with an existing taxonomy for information retrieval models. In addition, we investigated tools and services for CR management, to understand whether and how they addressed the topics identified.

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“…Previous work focused solely on software evolution and its influencing factors [1] [2], also in a predictive context [3] [4], but ignored solving this issue by the use of simulation methods. Simulation facilitates us to describe complex processes even with chaotic behavior in an elegant way and then to try out and compare different evolutionary scenarios [5].…”

Section: Introductionmentioning

confidence: 99%

Statistical Learning and Software Mining for Agent Based Simulation of Software Evolution

Honsel

2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

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In the process of software development it is of high interest for a project manager to gain insights about the ongoing process and possible development trends at several points in time. Substantial factors influencing this process are, e.g., the constellation of the development team, the growth and complexity of the system, and the error-proneness of software entities. For this purpose we build an agent based simulation tool which predicts the future of a project under given circumstances, stored in parameters, which control the simulation process.We estimate these parameters with the help of software mining. Our work exposed the need for a more fine-grained model for the developer behavior. Due to this we create a learning model, which helps us to understand the contribution behavior of developers and, thereby, to determine simulation parameters close to reality. In this paper we present our agent based simulation model for software evolution and describe how methods from statistical learning and data mining serves us to estimate suitable simulation parameters.

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Software Evolution Prediction Using Seasonal Time Analysis: A Comparative Study

Cited by 27 publications

References 22 publications

Automatic Means of Identifying Evolutionary Events in Software Development

Automatic Means of Identifying Evolutionary Events in Software Development

Challenges and opportunities for software change request repositories: a systematic mapping study

Statistical Learning and Software Mining for Agent Based Simulation of Software Evolution

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