Analogy-based Software Effort Estimation Research Articles

Appropriate number of analogues in analogy based software effort estimation using quality datasets

Appropriate number of analogues in analogy based software effort estimation using quality datasets

Stacking regularization in analogy-based software effort estimation.

Analogy-based estimation (ABE) estimates the effort of the current project based on the information of similar past projects. The solution function of ABE provides the final effort prediction of a new project. Many studies on ABE in the past have provided various solution functions, but its effectiveness can still be enhanced. The present study is an attempt to improve the effort prediction accuracy of ABE by proposing a solution function SABE: Stacking regularization in analogy-based software effort estimation. The core of SABE is stacking, which is a machine learning technique. Stacking is beneficial as it works on multiple models harnessing their capabilities and provides a better estimation accuracy as compared to a single model. The proposed method is validated on four software effort estimation datasets and compared with the already existing solution functions: closet analogy, mean, median and inverse distance weighted mean. The evaluation criteria used are mean magnitude of relative error (MMRE), median magnitude of relative error (MdMRE), prediction (PRED) and standard accuracy (SA). The results suggested that the SABE showed promising performance for almost all the evaluation criteria when compared with the results of the earlier studies.

Preliminary performance study of a brief review on machine learning techniques for analogy based software effort estimation

Preliminary performance study of a brief review on machine learning techniques for analogy based software effort estimation

On an optimal analogy-based software effort estimation

Context: An analogy-based software effort estimation technique estimates the required effort for a new software project based on the total effort used in completing past similar projects. In practice, offering high accuracy can be difficult for the technique when the new software project is not similar to any completed projects. In this case, the accuracy will rely heavily on a process called effort adaptation, where the level of difference between the new project and its most similar past projects is quantified and transformed to the difference in the effort. In the past, attempts to adapt to the effort used machine learning algorithms; however, no algorithm was able to offer a significantly higher performance. On the contrary, only a simple heuristic such as scaling the effort by consulting the difference in software size was adopted. Objective:More recently, million-dollar prize data-science competitions have fostered the rapid development of more powerful machine learning algorithms, such as the Gradient boosting machine and Deep learning algorithm. Therefore, this study revisits the comparison of software effort adaptors that are based on heuristics and machine learning algorithms. Method:A systematic comparison of software effort estimators, which they all were fully optimized by Bayesian optimization technique, was carried out on 13 standard benchmark datasets. The comparison was supported by robust performance metrics and robust statistical test methods. Conclusion:The results suggest a novel strategy to construct a more accurate analogy-based estimator by adopting a combined effort adaptor. In particular, the analogy-based model that adapts to the effort by integrating the Gradient boosting machine algorithm and a traditional adaptation technique based on productivity adjustment has performed the best in the study. Particularly, this model significantly outperformed various state-of-the-art effort estimation techniques, including a current standard benchmark algorithmic-based technique, analogy-based techniques, and machine learning-based techniques.

Uncertainty management in software effort estimation using a consistent fuzzy analogy-based method

Software effort estimation is a critical task in software project development management. Unfortunately, the uncertainty and inaccuracy are inherent properties of the software effort estimation environment. These are caused by the limited capabilities of the managers, to foresee, measure and describe factors influencing the software effort. The promising Fuzzy Analogy-based Software Effort Estimation model (FASEE) employs successfully fuzzy logic with approximate reasoning theory to handle imprecision and reasoning under uncertainty. Also, FASEE use possibility distribution to quantify the uncertainty in the estimate that aid the software managers to assess risks. Yet, the FASEE suffer from the low data quality and the uncertainty induced in the reasoning process. In this paper, we propose an enhancement of the FASEE, by imposing consistency criteria to deal with the aforementioned drawbacks. So, the underlying model, called Consistent Fuzzy Analogy-based Software Effort Estimation (C-FASEE) is endowed with two capabilities. The first one introduces consistency criteria in attribute representation by fuzzy sets to enable fitting each attribute to the software effort. The second one introduces a new relation of confidence to measure the extent that the resulted most similar projects respect the assumption “similar projects have similar efforts”. Moreover, the C-FASEE method provide a fuzzy estimate of the most possible fuzzy set will the true effort of the new software project falls in. This allow to the software manager to assess risks more optimally. The proposed C-FASEE is validated over thirteen software project datasets that represent different complexities. The obtained results are compared to variant methods of the analogy-based software effort estimation approach. The experimental results show that our proposal provides a good estimation accuracy of and has significantly best performance against the comparison methods.

An Evolutionary Computation Approach for Project Selection in Analogy based Software Effort Estimation

Objectives: Software effort estimation is a critical task in the software development process due to the intangible nature of software. A new model for software effort estimation using Differential Evolution Algorithm called DEAPS is proposed in this paper. Method: In this methodology, the complete set of historical project base is reduced to a set of similar projects using Euclidean distance metric. Then Differential Evolution Algorithm which is an Evolutionary Computation method is used for optimization and the most relevant project is retrieved. The proposed method is validated on Desharnais dataset. Findings: DE has a very effective mutation process which improves the ability of exploration. So we got promising results which indicate that the use of this model could significantly improve the efficiency of Analogy based Software Effort Estimation. The metrics used are MMRE, MdMRE and pred (25%). The results are compared with previous findings and the results clearly show that the proposed method is better than the existing methods. Application: This methodology can be used to minimize the errors in the software estimation so that financial loss and delay in the completion of project may be avoided.

A stability assessment of solution adaptation techniques for analogy-based software effort estimation

Among numerous possible choices of effort estimation methods, analogy-based software effort estimation based on Case-based reasoning is one of the most adopted methods in both the industry and rese...

SOFTWARE DEVELOPMENT EFFORT ESTIMATION USING CLASSICAL AND FUZZY ANALOGY: A CROSS-VALIDATION COMPARATIVE STUDY

Software effort estimation is one of the most important tasks in software project management. Of several techniques suggested for estimating software development effort, the analogy-based reasoning, or Case-Based Reasoning (CBR), approaches stand out as promising techniques. In this paper, the benefits of using linguistic rather than numerical values in the analogy process for software effort estimation are investigated. The performance, in terms of accuracy and tolerance of imprecision, of two analogy-based software effort estimation models (Classical Analogy and Fuzzy Analogy, which use numerical and linguistic values respectively to describe software projects) is compared. Three research questions related to the performance of these two models are discussed and answered. This study uses the International Software Benchmarking Standards Group (ISBSG) dataset and confirms the usefulness of using linguistic instead of numerical values in analogy-based software effort estimation models.

Analogy-based software effort estimation using Fuzzy numbers

Background Early stage software effort estimation is a crucial task for project bedding and feasibility studies. Since collected data during the early stages of a software development lifecycle is always imprecise and uncertain, it is very hard to deliver accurate estimates. Analogy-based estimation, which is one of the popular estimation methods, is rarely used during the early stage of a project because of uncertainty associated with attribute measurement and data availability. Aims We have integrated analogy-based estimation with Fuzzy numbers in order to improve the performance of software project effort estimation during the early stages of a software development lifecycle, using all available early data. Particularly, this paper proposes a new software project similarity measure and a new adaptation technique based on Fuzzy numbers. Method Empirical evaluations with Jack-knifing procedure have been carried out using five benchmark data sets of software projects, namely, ISBSG, Desharnais, Kemerer, Albrecht and COCOMO, and results are reported. The results are compared to those obtained by methods employed in the literature using case-based reasoning and stepwise regression. Results In all data sets the empirical evaluations have shown that the proposed similarity measure and adaptation techniques method were able to significantly improve the performance of analogy-based estimation during the early stages of software development. The results have also shown that the proposed method outperforms some well know estimation techniques such as case-based reasoning and stepwise regression. Conclusions It is concluded that the proposed estimation model could form a useful approach for early stage estimation especially when data is almost uncertain.

Analysis of attribute weighting heuristics for analogy-based software effort estimation method AQUA+

Estimation by analogy (EBA) predicts effort for a new project by aggregating effort information of similar projects from a given historical data set. Existing research results have shown that a careful selection and weighting of attributes may improve the performance of the estimation methods. This paper continues along that research line and considers weighting of attributes in order to improve the estimation accuracy. More specifically, the impact of weighting (and selection) of attributes is studied as extensions to our former EBA method AQUA, which has shown promising results and also allows estimation in the case of data sets that have non-quantitative attributes and missing values. The new resulting method is called AQUA+. For attribute weighting, a qualitative analysis pre-step using rough set analysis (RSA) is performed. RSA is a proven machine learning technique for classification of objects. We exploit the RSA results in different ways and define four heuristics for attribute weighting. AQUA+ was evaluated in two ways: (1) comparison between AQUA+ and AQUA, along with the comparative analysis between the proposed four heuristics for AQUA+, (2) comparison of AQUA+ with other EBA methods. The main evaluation results are: (1) better estimation accuracy was obtained by AQUA+ compared to AQUA over all six data sets; and (2) AQUA+ obtained better results than, or very close to that of other EBA methods for the three data sets applied to all the EBA methods. In conclusion, the proposed attribute weighing method using RSA can improve the estimation accuracy of EBA method AQUA+ according to the empirical studies over six data sets. Testing more data sets is necessary to get results that are more statistical significant.

The adjusted analogy-based software effort estimation based on similarity distances

Analogy-based estimation is a widely adopted problem solving method that has been evaluated and confirmed in software effort or cost estimation domains. The similarity measures between pairs of projects play a critical role in the analogy-based software effort estimation models. Such a model calculates a distance between the software project being estimated and each of the historical software projects, and then retrieves the most similar project for generating an effort estimate. Although there exist numerous analogy-based software effort estimation models in literature, little theoretical or experimental works have been reported on the method of deriving an effort estimate from the adjustment of the reused effort based on the similarity distance. The present paper investigates the effect on the improvement of estimation accuracy in analogy-based estimations when the genetic algorithm method is adopted to adjust reused effort based on the similarity distances between pairs of projects. The empirical results show that applying a suitable linear model to adjust the analogy-based estimations is a feasible approach to improving the accuracy of software effort estimates. It also demonstrates that the proposed model is comparable with those obtained when using other effort estimation methods.

Optimization of analogy weights by genetic algorithm for software effort estimation

A reliable and accurate estimate of software development effort has always been a challenge for both the software industry and academia. Analogy is a widely adopted problem solving technique that has been evaluated and confirmed in software effort or cost estimation domains. Similarity measures between pairs of effort drivers play a central role in analogy-based estimation models. However, hardly any research has addressed the issue of how to decide on suitable weighted similarity measures for software effort drivers. The present paper investigates the effect on estimation accuracy of the adoption of genetic algorithm (GA) to determine the appropriate weighted similarity measures of effort drivers in analogy-based software effort estimation models. Three weighted analogy methods, namely, the unequally weighted, the linearly weighted and the nonlinearly weighted methods are investigated in the present paper. We illustrate our approaches with data obtained from the International Software Benchmarking Standards Group (ISBSG) repository and the IBM DP services database. The experimental results show that applying GA to determine suitable weighted similarity measures of software effort drivers in analogy-based software effort estimation models is a feasible approach to improving the accuracy of software effort estimates. It also demonstrates that the nonlinearly weighted analogy method presents better estimate accuracy over the results obtained using the other methods.

An Empirical Study of Analogy-based Software Effort Estimation

Conventional approaches to software cost estimation have focused on algorithmic cost models, where an estimate of effort is calculated from one or more numerical inputs via a mathematical model. Analogy-based estimation has recently emerged as a promising approach, with comparable accuracy to algorithmic methods in some studies, and it is potentially easier to understand and apply. The current study compares several methods of analogy-based software effort estimation with each other and also with a simple linear regression model. The results show that people are better than tools at selecting analogues for the data set used in this study. Estimates based on their selections, with a linear size adjustment to the analogue‘s effort value, proved more accurate than estimates based on analogues selected by tools, and also more accurate than estimates based on the simple regression model.