Analogy-based software development effort estimation: A systematic mapping and review

Idri, Ali; Amazal, Fatima Azzahra; Abran, Alain

doi:10.1016/j.infsof.2014.07.013

Cited by 167 publications

(100 citation statements)

References 70 publications

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“…The most well‐known measures used to assess the accuracy of SDEE techniques are those based on the magnitude of relative error (MRE) criterion, such as MMRE and Pred( p ): MRE is calculated for each project in the dataset following Equation :

italicMRE = (||, \frac{{italicEffort}_{italicactual} - {italicEffort}_{italicestimated}}{{Effort}_{actual}}) \times 100

…”

Section: Introductionmentioning

confidence: 99%

Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation

Idri

Abnane

Abran

2017

J Software Evolu Process

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Software development effort estimation (SDEE) plays a primary role in software project management. But choosing the appropriate SDEE technique remains elusive for many project managers and researchers. Moreover, the choice of a reliable estimation accuracy measure is crucial because SDEE techniques behave differently given different accuracy measures. The most widely used accuracy measures in SDEE are those based on magnitude of relative error (MRE) such as mean/median MRE (MMRE/MedMRE) and prediction at level p (Pred(p)), which counts the number of observations where an SDEE technique gave MREs lower than p. However, MRE has proven to be an unreliable accuracy measure, favoring SDEE techniques that underestimate. Consequently, an unbiased measure called standardized accuracy (SA) has been proposed. This paper deals with the Pred(p) and SA measures. We investigate (1) the consistency of Pred(p) and SA as accuracy measures and SDEE technique selectors, and (2) the relationship between Pred(p) and SA. The results suggest that Pred(p) is less biased towards underestimates and generally selects the same best technique as SA. Moreover, SA and Pred(p) measure different aspects of technique performance, and SA may be used as a predictor of Pred(p) by means of the 3 association rules.

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italicMRE = (||, \frac{{italicEffort}_{italicactual} - {italicEffort}_{italicestimated}}{{Effort}_{actual}}) \times 100

…”

Section: Introductionmentioning

confidence: 99%

Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation

Idri

Abnane

Abran

2017

J Software Evolu Process

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“…The effort estimations were generally based on the following: analogy approach (Walkerden and Jeffery 1999;Kocaguneli et al 2012;Phannachitta et al 2014;Azzeh et al 2015;Idri et al 2015); expert judgment approach (Bottom-up or top-down estimation) (Jørgensen 2004(Jørgensen , 2005; Delphi methods, wideband Delphi approach (Moløkken-Østvold, and Jørgensen 2004;Cunha et al 2011);andCOCOMO model of 1980 s (Han et al 2015). Lastly, there are first order, second order and third order model effort estimation approaches as compressively analyzed by NCCA and AFCAA (2008).…”

Section: Effort Estimation Approach From Software Industrymentioning

confidence: 99%

Workflow Development Effort Estimation as Applied to Web Human Resource Management

Raheem

2016

Glob J Flex Syst Manag

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Over the past decade most of engineers, researchers, institutions, standard organizations and businessmen had been faced with challenges on how to represent productivity functions for effort estimation in information system workflow development. Despite the fact that workflows are everywhere, only few reports about its development effort are available. Accurate workflow sizing is a very difficult task at conceptual state. This paper proposes the proximity scoring measurement method to determine workflow size and to estimate workflow development effort with its knowledge retention effort. The approach is applied to the web publishing industry as a human resources case study. The approach of this paper is scalable and easy to apply to evaluate Web human resource workflow development effort. The implication of this work is that workflows can be estimated more precisely, helping to estimate the benefit of workflow reuse.

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“…The QA questionnaire contained six questions. Similar criteria were used in [11] and [13]: a) Are the aims of the research clearly defined? Yes (+1) if the authors gives clear motivations of the study, Partially (+0.5) if the authors gives unclear motivations of the study, and No (+0) if the authors gives no motivation of the study.…”

Section: Quality Assessmentmentioning

confidence: 99%

“…The publication source and channel of the papers selected respond to MQ1, while the publication year responds to MQ2. Regarding MQ3, a research type can be classified as in [11,13]: Evaluation Research, Solution Proposal or Other (e.g. Opinion papers).…”

Section: E Data Extractionmentioning

confidence: 99%

Systematic mapping study of missing values techniques in software engineering data

Idri¹,

Abnane²,

Abran

2015

2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distribu

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Missing Values (MV) present a serious problem facing research in software engineering (SE) which is mainly based on statistical and/or data mining analysis of SE data. The simple method of dealing with MV is to ignore data with missing observations. This leads to losing valuable information and then obtaining biased results. Therefore, various techniques have been developed to deal adequately with MV, especially those based on imputation methods. In this paper, a systematic mapping study was carried out to summarize the existing techniques dealing with MV in SE datasets and to classify the selected studies according to six classification criteria: research type, research approach, MV technique, MV type, data types and MV objective. Publication channels and trends were also identified. As results, 35 papers concerning MV treatments of SE data were selected. This study shows an increasing interest in machine learning (ML) techniques especially the K-nearest neighbor algorithm (KNN) to deal with MV in SE datasets and found that most of the MV techniques are used to serve software development effort estimation techniques.

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Analogy-based software development effort estimation: A systematic mapping and review

Cited by 167 publications

References 70 publications

Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation

Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation

Workflow Development Effort Estimation as Applied to Web Human Resource Management

Systematic mapping study of missing values techniques in software engineering data

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