Cluster Analysis &amp; Pso for Software Cost Estimation

Sethi, Tegjyot Singh; Hari, Ch. V. M. K.; Kaushal, Bindu; Sharma, Abhishek

doi:10.1007/978-3-642-20573-6_47

Cited by 4 publications

(7 citation statements)

References 5 publications

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“…For the purpose of experimentation, the SEEPC toolbox was provided with a training set of 36 values [13] derived from the COCOMO 81 dataset. Upon training the model, 3 clusters were obtained.…”

Section: Experimentation and Sample Resultsmentioning

confidence: 99%

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SEEPC: A Toolbox for Software Effort Estimation using Soft Computing Techniques

.CH.V.M.K¹,

Sethi²,

Jagadeesh.M³

2011

IJCA

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Software Effort estimation is the process of gauging the amount of effort required to complete the project. With the proliferation of software projects and the heterogeneity in there genre, there is a need for efficient software effort estimation techniques to enable the project managers to perform proper planning of the Software Life Cycle activates. In this article, a new hybrid toolbox based on soft computing techniques for effort estimation is introduced. Particle swarm optimization and cluster analysis has been implemented to perform efficient estimation of effort values with learning ability. The main aim of the toolbox is to provide an efficient, flexible and user friendly way of performing the effort estimation task, by catering to the needs of both the technical and the nontechnical users. The toolbox also implements the COCOMO model to enable a comparative analysis of the proposed model. It was observed that the model when provided with enough training data gave better results when compared with the standard COCOMO values

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Section: Experimentation and Sample Resultsmentioning

confidence: 99%

“…The MARE values are shown in Table 2. The SEEPC model makes use of efficient clustering and PSO to learn and develop in its course of usage [13]. As such, it is more efficient for making long term accurate predictions than the traditional COCOMO model.…”

Section: Experimentation and Sample Resultsmentioning

confidence: 99%

SEEPC: A Toolbox for Software Effort Estimation using Soft Computing Techniques

.CH.V.M.K¹,

Sethi²,

Jagadeesh.M³

2011

IJCA

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“…Compared to previous algorithms, the proposed algorithm has demonstrated superior results in cost prediction. The International Software Benchmarking Standards Group (ISBSG) dataset has been utilized for a linear regression model based on the criteria of relative error values [10]. The ISBSG dataset includes 3042 software projects from different companies during the last 6 years.…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Software Cost Estimation with a Hybrid Tabu Search and Invasive Weed Optimization Algorithms

Mohamedyusf,

Sharif,

Ghareb

2024

UHD J SCI TECH

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Due to the ever-increasing progress of software projects and their widespread impact on all industries, models must be designed and implemented to analyze and estimate costs and time. Until now, most of the software cost estimation (SCE) has been based on the analyst’s experiences and similar projects and these models are often inaccurate and inappropriate. The project will not be finished in the specified time and will include additional costs. Algorithmic models such as COCOMO are not very accurate in SCE. They are linear and the appropriate value for effort factors is not considered. On the other hand, artificial intelligence models have made significant progress in the cost estimation modeling of software projects in the past three decades. These models determine the correct value for effort factors through iteration and training, providing a more accurate estimate compared to algorithmic models. This paper employs a hybrid model incorporating the Tabu Search (TS) algorithm and the Invasive Weed Optimization (IWO) algorithm for SCE. IWO algorithm solutions are improved using the TS algorithm. The NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets are used for the evaluation. The proposed model has been able to reduce the MMRE rate compared to the IWO algorithm and the TS algorithm. The proposed model on the NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets obtained values of MMRE of 15.43, 17.05, 28.75, 58.43, and 22.46, respectively.

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“…r Theme relevance-There has been a growth in the number of software projects (Sethi et al, 2011;Bazaz et al, 2012); project risk in this segment (Bazaz et al, 2012); and in the use of Scrum (VersionOne, 2016), which is the most popular agile methodology (Azizyan et al, 2011;Meier and Ivarsson, 2013;Mundra et al, 2013;VersionOne, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…This research is conducted for the following reasons: Theme relevance—There has been a growth in the number of software projects (Sethi et al., ; Bazaz et al, ); project risk in this segment (Bazaz et al., ); and in the use of Scrum (VersionOne, ), which is the most popular agile methodology (Azizyan et al., ; Meier and Ivarsson, ; Mundra et al., ; VersionOne, ). Use of risk management in software projects—Generally, it is observed that there exists a lack of risk management in software projects using Scrum (Hijazi et al., ; Tomanek and Juricek, ), and software projects in general (Bannerman, ; Junior et al., ; Singh et al., ). Software project success—Risk management can increase the chance of success in software projects (SEI, ; Chowdhury and Arefeen, ; Bannerman, ) by reducing their uncertainties and chances of failure (Wet and Visser, ). On the other hand, the absence of risk management procedures is one of the reasons for the failure of these projects (Charette, ; Dey et al., ; Lu et al., ). Characteristics of risk management—Agile methodologies do not explicitly suggest risk management processes (Nelson et al., ; Nyfjord and Kajko‐Mattsson, ; Khatri et al., ; Moran, ). …”

Section: Introductionmentioning

confidence: 99%

Risk management analysis in Scrum software projects

Tavares

Silva

Souza

2017

Int Trans Operational Res

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One of the reasons for the failure of software projects is the absence of risk management procedures or its improper application. The adoption of Scrum in software projects is increasing. However, such approach does not specify risk management activities. This paper presents the results of a survey conducted using a qualitative approach to analyze how risk management is carried out in Scrum software projects. Consequently, we present risk management practices that achieved greater and lesser agreement among respondents and the literature, respectively. We found that risk management must be applied continuously in a feedback loop. Furthermore, Scrum projects must not have a high formal planning level, even for high‐risk ones. The research verified that risk management in Scrum is performed differently from its application in traditional approaches. The framework has native resources, but classic processes of risk management would be incorporated and adapted.

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Cluster Analysis & Pso for Software Cost Estimation

Cited by 4 publications

References 5 publications

SEEPC: A Toolbox for Software Effort Estimation using Soft Computing Techniques

SEEPC: A Toolbox for Software Effort Estimation using Soft Computing Techniques

A New Approach for Software Cost Estimation with a Hybrid Tabu Search and Invasive Weed Optimization Algorithms

Risk management analysis in Scrum software projects

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