Domain specific phase by phase effort estimation in software projects

Kultur, Yigit; Kocagüneli, Ekrem; Bener, Ayşe

doi:10.1109/iscis.2009.5291873

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Software project data for effort prediction purposes have been widely studied and many models have been proposed, such as COCOMO81 (Boehm, 1981), COCOMO II and COCOT (Boehm et al, 2000) and RUPS (Kruchten, 1999). The effort values can vary widely in real life environment as found by Kultur et al (2009) and Tan et al (2011). We referred to COCOMO II model for our simulation.…”

Section: Team Productivity and Error Injection Sub-sectormentioning

confidence: 96%

A study of software development project cost, schedule and quality by outsourcing to low cost destination

Mishra

Mahanty

2016

Journal of Enterprise Information Management

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Purpose – The purpose of this paper is to find good values of onsite-offshore team strength; number of hours of communication between business users and onsite team and between onsite and offshore team so as to reduce project cost and improve schedule in a global software development (GSD) environment for software development project. Design/methodology/approach – This study employs system dynamics simulation approach to study software project characteristics in both co-located and distributed development environments. The authors consulted 14 experts from Indian software outsourcing industry during our model construction and validation. Findings – The study results show that there is a drop in overall team productivity in outsourcing environment by considering the offshore options. But the project cost can be reduced by employing the offshore team for coding and testing work only with minimal training for imparting business knowledge. The research results show that there is a potential to save project cost by being flexible in project schedule. Research limitations/implications – The implication of the study is that the project management team should be careful not to keep high percentage of manpower at offshore location in distributed software environment. A large offshore team can increase project cost and schedule due to higher training overhead, lower productivity and higher error proneness. In GSD, the management effort should be to keep requirement analysis and design work at onsite location and involves the offshore team in coding and testing work. Practical implications – The software project manager can use the model results to divide the software team between onsite and offshore location during various phases of software development in distributed environment. Originality/value – The study is novel as there is little attempt at finding the team distribution between onsite and offshore location in GSD environment.

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Section: Team Productivity and Error Injection Sub-sectormentioning

confidence: 96%

A study of software development project cost, schedule and quality by outsourcing to low cost destination

Mishra

Mahanty

2016

Journal of Enterprise Information Management

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“…7 Most of the studies on effort prediction involve all of the SDLC phases. 19 The three systematic reviews of studies on effort prediction 3,7,20 have explicitly as exclusion criteria to studies predicting either maintenance or testing effort phases. A study published in 2020 identifies only 16 studies published between 1995 and 2018 on maintenance effort prediction.…”

Section: Studies On Software Effort Prediction Techniquesmentioning

confidence: 99%

“…18 In software organizations, it is common to find one team assigned by phase; however, most prediction models have been generated from data related to the effort spent in all of the SDLC phases. 19 The published systematic reviews on hundreds of studies about SDLC effort prediction have been built having as exclusion criterion those studies aimed at specific phases such as testing or maintenance. 3,6,7,20 A few studies have proposed models to predict the effort of phases such as testing 21 or maintenance 22 ; however, I did not find any study whose objective has been to propose a model to predict specifically the construction effort (CE).…”

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confidence: 99%

Effort prediction for the software project construction phase

López-Martín

2021

J Software Evolu Process

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The construction phase effort prediction is needed for assigning resources to teams of practitioners destined specifically to this phase of the software development life cycle (SDLC). Construction effort (CE) has been reported between 27.5% and 58% of the total SDLC effort causing the uncertainty of taking these percentages as reference. A support vector regression (SVR) training involves quadratic programming problems that can analytically be solved using a sequential minimal optimization (SMO) algorithm. Moreover, a Pearson VII (PUK) kernel is useful to replace a set of kernel functions commonly used by a SVR. The objective of this study is to apply the SMO with the PUK to train SVR for predicting CE. The SVR model trained with the SMO algorithm having as kernel to the PUK (SVR-SMO-PUK) prediction accuracy was statistically compared to those accuracies obtained from statistical regression (SR), neural network (NN), and two types of SVR. Seven international public data sets of software projects were used. Results showed that the SVR-SMO-PUK was better than the SR in five data sets and better than NN in two of these five data sets. It was equal than SR and NN in the remaining two data sets. It was equal than ε-SVR and ʋ-SVR in the seven data sets. Thus, the SVR-SMO-PUK is useful to software managers to predict CE.

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Kernel methods for software effort estimation

Kocagüneli

Keung

2011

Empir Software Eng

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Domain specific phase by phase effort estimation in software projects

Cited by 6 publications

References 11 publications

A study of software development project cost, schedule and quality by outsourcing to low cost destination

A study of software development project cost, schedule and quality by outsourcing to low cost destination

Effort prediction for the software project construction phase

Kernel methods for software effort estimation

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