Improving the accuracy of COCOMO's effort estimation based on neural networks and fuzzy logic model

Sarno, Riyanarto; Sidabutar, Johannes; Sarwosri,

doi:10.1109/icts.2015.7379898

Cited by 19 publications

(16 citation statements)

References 4 publications

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“…In the paper, Idri et al (2000) proposed an adaptation of COCOMO using Fuzzy Logic. The same approach was discussed by Sheta and Aljahdali (2013) and Sarno et al (2015). In Boehm and Valerdi (2008), many variations of COCOMO over the years and new methods that were derived from it due to specific calibrations for a given segment were presented.…”

Section: Related Workmentioning

confidence: 96%

Applying COCOMO II for a DO-178C Safety-Critical Software Effort Estimation

Santos

Ferreira

2019

J.Aerosp. Technol. Manag.

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This paper provides a real example of applying COCOMO II as an estimation technique for the required software development effort in a safety-critical software application project following the DO-178C processes. The main goal and contribution of the case study is to support the research on software effort estimation and to provide software practitioners with useful data based on a real project. We applied the method as it is, by correlating the effort multiplier factors with the complexity and objectives introduced by the DO-178C level A application, resulting in an estimated effort. The rationales for each scale factor and effort multiplier selection were also described in detail. By comparing the estimated values with the actual required data, we found a magnitude of relative error (MRE) of 40% and provided alternatives for future work in order to increase the effort estimation accuracy in safety-critical software projects.

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Section: Related Workmentioning

confidence: 96%

Applying COCOMO II for a DO-178C Safety-Critical Software Effort Estimation

Santos

Ferreira

2019

J.Aerosp. Technol. Manag.

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“…Previous studies identified factors that may affect maintenance cost (Buchmann et al 2011;Correa et al 2016;Li et al 2010;Nguyen et al 2011;Ren et al 2011a;Tsu-noda et al 2015) and provided methods to estimate the development cost of software (April 2010;Matijevic et al 2012;Ng, Gable 2010;Ren et al 2011b;Salmeron, Lopez 2012;Sarno et al 2015). Several previous studies have proposed a method for the successful implementation of an ES, though these are not directly related to maintenance (Hadidi et al 2017;Niu et al 2013Niu et al , 2014Soja et al 2015;Wu et al 2009;Xu 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Causes and Level of Maintenance for Enterprise Systems in Construction Companies

Lee

Lee²

2018

Journal of Civil Engineering and Management

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A construction company without a similar information technology (IT) system in the past has insufficient historical data to use for investment decision-making of IT system. An estimation of maintenance costs is especially more uncertain than the initial investment costs, and the uncertainty is greater when the IT system is used over a long period, such as an enterprise system (ES). This study proposes estimation criteria for the maintenance costs of an ES for an accurate investment decision. First, the causes of maintenance are determined, and the level of maintenance analyzed. Then, the result is compared with a general trend of maintenance incidence (bathtub curve) that is widely used as reference criteria to estimate maintenance cost. The level of maintenance was high during the early stage but steadily decreased in the middle and end stage because high-cost maintenance activities were postponed with the approach of the time in which the ES was being restructured. This trend is different from the bathtub curve that increases again during the end stage. Thus, when a maintenance contract is negotiated, the maintenance level that affects maintenance cost should be considered as well as the incidence of maintenance.

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“…Non-algorithmic method tries to construct rules that fit to the data. These include analogy method [19]. artificial neural network [20], fuzzy [21], and genetic algorithm.…”

Section: A Software Cost Estimationmentioning

confidence: 99%

“…Riyanarto and Johannes [19] [20] investigated the role of Effort Multiplier (EM) and Line of Code (LOC) to utilized the effort estimation. Gaussian Membership Function (GMF) [9] has been applied to the COCOMO II to represent the EM.…”

Section: Related Workmentioning

confidence: 99%

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Optimizing Effort and Time Parameters of COCOMO II Estimation using Fuzzy Multi-objective PSO

Langsari

Sarno

2017

EECSI

Self Cite

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The estimation of software effort is an essential and crucial activity for the software development life cycle. Software effort estimation is a challenge that often appears on the project of making a software. A poor estimate will produce result in a worse project management. Various software cost estimation model has been introduced to resolve this problem. Constructive Cost Model II (COCOMO II Model) create large extent most considerable and broadly used as model for cost estimation. To estimate the effort and the development time of a software project, COCOMO II model uses cost drivers, scale factors and line of code. However, the model is still lacking in terms of accuracy both in effort and development time estimation. In this study, we do investigate the influence of components and attributes to achieve new better accuracy improvement on COCOMO II model. And we introduced the use of Gaussian Membership Function (GMF) Fuzzy Logic and Multi-Objective Particle Swarm Optimization method (MOPSO) algorithms in calibrating and optimizing the COCOMO II model parameters. The proposed method is applied on Nasa93 dataset. The experiment result of proposed method able to reduce error down to 11.891% and 8.082% from the perspective of COCOMO II model. The method has achieved better results than those of previous researches and deals proficient with inexplicit data input and further improve reliability of the estimation method.

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Improving the accuracy of COCOMO's effort estimation based on neural networks and fuzzy logic model

Cited by 19 publications

References 4 publications

Applying COCOMO II for a DO-178C Safety-Critical Software Effort Estimation

Applying COCOMO II for a DO-178C Safety-Critical Software Effort Estimation

Analysis of the Causes and Level of Maintenance for Enterprise Systems in Construction Companies

Optimizing Effort and Time Parameters of COCOMO II Estimation using Fuzzy Multi-objective PSO

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