The state‐of‐the‐art in software development effort estimation. Issue 12 (29th August 2018)

• Record Type: Journal Article
• Authors: Gautam, Swarnima Singh; Singh, Vrijendra
• Editors: Darren, Dalcher
• Is Part Of: Journal of software. Volume 30:Issue 12(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-29
• Subjects: effort estimation -- software development -- software engineering -- software estimation; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1983
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: The software developers and researchers have been facing difficulties regarding software development effort estimation (SDEE) since 1960s. Both overestimation and underestimation are problematic for future software development. The software engineering field is continuously adapting new technologies and development methodologies, so there is always a requirement to have an accurate SDEE method that can cater the needs of continually growing software industry. The major purpose of this state‐of‐the‐art review is to provide an additional insight of existing SDEE studies while considering five points of reference: techniques used to construct models, strengths and weaknesses of different models, availability of benchmark data sets, data set characteristics, generalization ability of models. We have performed a comprehensive review of SDEE studies published in the period 1981‐2016. We have defined a new scheme of categorizing existing SDEE models. We have found that a majority of available data sets do not include complete information of projects, which misleads the direction of research. To compare SDEE models, we recommend to use same data sets while focusing on specific aspects of accuracy as none of SDEE studies has yet been able to compare all the existing models over same data sets while considering same aspects of accuracy. Abstract : This state‐of‐the‐art review provides an additional insight of existing SDEE models while considering five points of reference: underlying technique, strengths and weaknesses of different models, availability of data sets, data set characteristics, and generalization ability of models. We have performed a comprehensive review of SDEE models. We have presented a new way to categorize existing SDEE models. In order to compare SDEE models, we recommend to use same data sets while focusing on specific aspects of accuracy.