Software Evolution Prediction Using Seasonal Time Analysis: a Comparative Study

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

Abstract –Prediction models of software change requests are useful for supporting rational and timely resource allocation to the evolution process. In this paper we use a time series forecasting model to predict software maintenance and evolution requests in an open source software project (Eclipse), as an example of projects with seasonal release cycles. We build an ARIMA model based on data collected from Eclipse’s change request tracking system since the project’s start. A change request may refer to defects found in the software, but also to suggested improvements in the system under scrutiny. Our model includes the identification of seasonal patterns and tendencies, and is validated through the forecast of the change requests evolution for the next 12 months. The usage of seasonal information significantly improves the estimation ability of this model, when compared to other ARIMA models found in the literature, and does so for a much longer estimation period. Being able to accurately forecast the change requests’ evolution over a fairly long time period is an important ability for enabling adequate process control in maintenance activities, and facilitates effort estimation and timely resources allocation. The approach presented in this paper is suitable for projects with a relatively long history, as the model building process relies on historic data.
Original languageUnknown
Title of host publicationEuropean Conference on Software Maintenance and Reengineering
Pages213-222
DOIs
Publication statusPublished - 1 Jan 2012
EventEuropean Conference on Software Maintenance and Reengineering -
Duration: 1 Jan 2012 → …

Conference

ConferenceEuropean Conference on Software Maintenance and Reengineering
Period1/01/12 → …

Keywords

    Cite this

    @inproceedings{255abfcc3e21454faa06571cc772c4eb,
    title = "Software Evolution Prediction Using Seasonal Time Analysis: a Comparative Study",
    abstract = "Abstract –Prediction models of software change requests are useful for supporting rational and timely resource allocation to the evolution process. In this paper we use a time series forecasting model to predict software maintenance and evolution requests in an open source software project (Eclipse), as an example of projects with seasonal release cycles. We build an ARIMA model based on data collected from Eclipse’s change request tracking system since the project’s start. A change request may refer to defects found in the software, but also to suggested improvements in the system under scrutiny. Our model includes the identification of seasonal patterns and tendencies, and is validated through the forecast of the change requests evolution for the next 12 months. The usage of seasonal information significantly improves the estimation ability of this model, when compared to other ARIMA models found in the literature, and does so for a much longer estimation period. Being able to accurately forecast the change requests’ evolution over a fairly long time period is an important ability for enabling adequate process control in maintenance activities, and facilitates effort estimation and timely resources allocation. The approach presented in this paper is suitable for projects with a relatively long history, as the model building process relies on historic data.",
    keywords = "Software Evolution, Eclipse, Change requests prediction model, ARIMA, Bugzilla",
    author = "Goul{\~a}o, {Miguel Carlos Pacheco Afonso} and Abreu, {Fernando Manuel Pereira da Costa Brito E}",
    year = "2012",
    month = "1",
    day = "1",
    doi = "10.1109/CSMR.2012.30",
    language = "Unknown",
    isbn = "978-1-4673-0984-4",
    pages = "213--222",
    booktitle = "European Conference on Software Maintenance and Reengineering",

    }

    Goulão, MCPA & Abreu, FMPDCBE 2012, Software Evolution Prediction Using Seasonal Time Analysis: a Comparative Study. in European Conference on Software Maintenance and Reengineering. pp. 213-222, European Conference on Software Maintenance and Reengineering, 1/01/12. https://doi.org/10.1109/CSMR.2012.30

    Software Evolution Prediction Using Seasonal Time Analysis: a Comparative Study. / Goulão, Miguel Carlos Pacheco Afonso; Abreu, Fernando Manuel Pereira da Costa Brito E.

    European Conference on Software Maintenance and Reengineering. 2012. p. 213-222.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    N2 - Abstract –Prediction models of software change requests are useful for supporting rational and timely resource allocation to the evolution process. In this paper we use a time series forecasting model to predict software maintenance and evolution requests in an open source software project (Eclipse), as an example of projects with seasonal release cycles. We build an ARIMA model based on data collected from Eclipse’s change request tracking system since the project’s start. A change request may refer to defects found in the software, but also to suggested improvements in the system under scrutiny. Our model includes the identification of seasonal patterns and tendencies, and is validated through the forecast of the change requests evolution for the next 12 months. The usage of seasonal information significantly improves the estimation ability of this model, when compared to other ARIMA models found in the literature, and does so for a much longer estimation period. Being able to accurately forecast the change requests’ evolution over a fairly long time period is an important ability for enabling adequate process control in maintenance activities, and facilitates effort estimation and timely resources allocation. The approach presented in this paper is suitable for projects with a relatively long history, as the model building process relies on historic data.

    AB - Abstract –Prediction models of software change requests are useful for supporting rational and timely resource allocation to the evolution process. In this paper we use a time series forecasting model to predict software maintenance and evolution requests in an open source software project (Eclipse), as an example of projects with seasonal release cycles. We build an ARIMA model based on data collected from Eclipse’s change request tracking system since the project’s start. A change request may refer to defects found in the software, but also to suggested improvements in the system under scrutiny. Our model includes the identification of seasonal patterns and tendencies, and is validated through the forecast of the change requests evolution for the next 12 months. The usage of seasonal information significantly improves the estimation ability of this model, when compared to other ARIMA models found in the literature, and does so for a much longer estimation period. Being able to accurately forecast the change requests’ evolution over a fairly long time period is an important ability for enabling adequate process control in maintenance activities, and facilitates effort estimation and timely resources allocation. The approach presented in this paper is suitable for projects with a relatively long history, as the model building process relies on historic data.

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