Flower Pollination Algorithm for Software Effort Coefficients Optimization to Improve Effort Estimation Accuracy

2021 ◽  
Vol 9 (2) ◽  
pp. 139
Author(s):  
Alifia Puspaningrum ◽  
Fachrul Pralienka Bani Muhammad ◽  
Esti Mulyani
Keyword(s):  
Search Algorithm ◽  
Cost Model ◽  
Cuckoo Search ◽  
Estimation Accuracy ◽  
Flower Pollination Algorithm ◽  
Metaheuristic Algorithm ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Flower Pollination ◽  
Cocomo Ii

Software effort estimation is one of important area in project management which used to predict effort for each person to develop an application. Besides, Constructive Cost Model (COCOMO) II is a common model used to estimate effort estimation. There are two coefficients in estimating effort of COCOMO II which highly affect the estimation accuracy. Several methods have been conducted to estimate those coefficients which can predict a closer value between actual effort and predicted value.  In this paper, a new metaheuristic algorithm which is known as Flower Pollination Algorithm (FPA) is proposed in several scenario of iteration. Besides, FPA is also compared to several metaheuristic algorithm, namely Cuckoo Search Algorithm and Particle Swarm Optimization. After evaluated by using Mean Magnitude of Relative Error (MMRE), experimental results show that FPA obtains the best result in estimating effort compared to other algorithms by reached 52.48% of MMRE in 500 iterations.

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

2019 ◽  
Author(s):  
Lucas Pereira dos Santos ◽  
Maurício Ferreira
Keyword(s):  
Estimation Accuracy ◽  
Development Effort ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Software Application ◽  
Safety Critical ◽  
Software Development Effort ◽  
Software Practitioners ◽  
Cocomo Ii

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.

scholarly journals Software Quality Assesment using COCOMO-II Metrics with ABC and NN

2020 ◽  
Vol 9 (3) ◽  
pp. 2982-2988
Keyword(s):  
Cost Model ◽  
Small Variation ◽  
Effort Estimation ◽  
Quality Estimation ◽  
Bee Colony ◽  
Proposed Model ◽  
Software Development System ◽  
Model Author ◽  
Key Aspects ◽  
Cocomo Ii

Time, cost and quality predictions are the key aspects of any software development system. Loses that result due to wrong estimations may lead to irresistible damage. It is observed that a badly estimated project always results into a bad quality output as the efforts are put in the wrong direction. In the present study, author proposed ABC-COCOMO-II as a new model and tried to enhance the extent of accuracy in effort quality assessment through effort estimation. In the proposed model author combined the strengths of COCOMO-II (Constructive Cost Model) with the Artificial Bee Colony (ABC) and Neural Network (NN). In the present work, ABC algorithm is used to select the best solution, NN is used for the classification purpose to improve the quality estimation using COCOMO-II. The results are compared and evaluated with the pre-existing effort estimation models. The simulation results had shown that the proposed combination outperformed in terms of quality estimation with small variation of 5-10% in comparison to the actual effort, which further leads to betterment of the quality. More than 90% projects results into high quality output for the proposed algorithmic architecture.

Regression Analysis Based Software Effort Estimation Method

2016 ◽  
Vol 26 (05) ◽  
pp. 807-826 ◽  
Author(s):  
Fatih Yücalar ◽  
Deniz Kilinc ◽  
Emin Borandag ◽  
Akin Ozcift
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Estimation Method ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Development Effort ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation

Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simple linear regression based effort estimation method developed by Nassif et al. In order to evaluate and compare the proposed method, the data of 10 software projects developed by four well-established software companies in Turkey were collected and datasets were created. When effort estimations obtained from datasets and actual efforts spent to complete the projects are compared with each other, it has been observed that the proposed method has higher effort estimation accuracy compared to the other methods.

Nature-Inspired Algorithms for Bi-Criteria Parallel Machine Scheduling

2019 ◽  
pp. 122-148
Author(s):  
Kawal Jeet
Keyword(s):  
Parallel Machines ◽  
Cuckoo Search ◽  
Parallel Machine Scheduling ◽  
Flower Pollination Algorithm ◽  
Grey Wolf Optimizer ◽  
Nsga Ii ◽  
Local Optima ◽  
Flower Pollination ◽  
Bee Colony ◽  
Nature Inspired Algorithms

Nature has always been a source of inspiration for human beings. Nature-inspired search-based algorithms have an enormous computational intelligence and capabilities and are observing diverse applications in engineering and manufacturing problems. In this chapter, six nature-inspired algorithms, namely artificial bee colony, bat, black hole, cuckoo search, flower pollination, and grey wolf optimizer algorithms, have been investigated for scheduling of multiple jobs on multiple potential parallel machines. Weighted flow time and tardiness have been used as optimization criteria. These algorithms are very efficient in identifying optimal solutions, but as the size of the problem increases, these algorithms tend to get stuck at local optima. In order to extract these algorithms from local optima, genetic algorithm has been used. Flower pollination algorithm, when appended with GA, is observed to perform better than other counterpart nature-inspired algorithms as well as existing heuristics and meta-heuristics based on MOGA and NSGA-II algorithms.

A Variant of COCOMO II for Improved Software Effort Estimation

2014 ◽  
Vol 6 (4) ◽  
pp. 346-350
Author(s):  
Ziyad T. Abdulmehdi ◽  
M. S. Saleem Basha ◽  
Mohamed Jameel ◽  
P. Dhavachelvan
Keyword(s):  
Effort Estimation ◽  
Software Effort Estimation ◽  
Cocomo Ii

scholarly journals Enhancing Delphi Method with Algorithmic Estimates for Software Effort Estimation: An Experimental Study

2021 ◽  
Vol 12 (04) ◽  
pp. 01-18
Author(s):  
Tharwon Arnuphaptrairong
Keyword(s):  
Delphi Method ◽  
Cost Estimation ◽  
Expert Judgment ◽  
Delphi Process ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Delphi Experiment ◽  
Individual Expert

Literature review shows that more accurate software effort and cost estimation methods are needed for software project management success. Expert judgment and algorithmic model estimation are two predominant methods discussed in the literature. Both are reported almost at the comparable level of accuracy performance. The combination of the two methods is suggested to increase the estimation accuracy. Delphi method is an encouraging structured expert judgment method for software effort group estimation but surprisingly little was reported in the literature. The objective of this study is to test if the Delphi estimates will be more accurate if the participants in the Delphi process are exposed to the algorithmic estimates. A Delphi experiment where the participants in the Delphi process were exposed to three algorithmic estimates –Function Points, COCOMO estimates, and Use Case Points, was therefore conducted. The findings show that the Delphi estimates are slightly more accurate than the statistical combination of individual expert estimates, but they are not statistically significant. However, the Delphi estimates are statistically significant more accurate than the individual estimates. The results also show that the Delphi estimates are slightly less optimistic than the statistical combination of individual expert estimates but they are not statistically significant either. The adapted Delphi experiment shows a promising technique for improving the software cost estimation accuracy.

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