A HYBRID SOFTWARE COST ESTIMATION APPROACH UTILIZING DECISION TREES AND FUZZY LOGIC

Software cost estimation is one of the most challenging activities in software project management. Since the software cost estimation affects almost all activities of software project development such as: biding, planning, and budgeting, the accurate estimation is very crucial to the success of software project management. However, due to the inherent uncertainties in the estimation process and other factors, the accurate estimates are often obtained with great difficulties. Therefore, it is safer to generate interval based estimates with a certain probability over them. In the literature, many approaches have been proposed for interval estimation. In this study, we propose a navel method namely Analogy Based Sampling (ABS) and compare ABS against the well established Bootstrapped Analogy Based Estimation (BABE) which is the only existing variant of analogy based method with the capability to generate interval predictions. The results and comparisons show that ABS could improve the performance of BABE with much higher efficiencies and more accurate interval predictions.

Software cost and time estimation is the process of estimating the cost and time required to develop a software system. Software cost and time estimation supports the planning and tracking of software projects. Effectively controlling the expensive investment of software development is one of the important issues in software project management. Estimating software development cost with high precision is still a great challenge for project managers, because it allows for considerable financial and strategic planning. Software cost estimation refers to the predictions of the likely amount of effort, time, and staffing levels required to build a software system. A very helpful form of cost estimation is the one made at an early stage during a project, when the costing of the project is proposed for approval. However, estimates at the early stages of the development are the most difficult to obtain. In this paper a novel Constructive Cost Model (COCOMO) based on soft computing approach is proposed for software cost estimation. This model carries some of the desirable features of neural networks approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural networks approach, the proposed model can be interpreted and validated by experts, and has good generalisation capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. From the experimental results, it was concluded that, by the proposed neural network model, the accuracy of cost estimation can be improved and the estimated cost can be very close to the actual cost.

The development effort estimation is one of the most difficult problems in software project management. It is one of the most critical aspects in the early stages of the software project. Several software development effort estimation models have been proposed, however, these models are not able to obtain more than a 25 percent of accuracy, neither provide an understandable model for experts in the application area. Therefore, in this paper, we present EEpred, an explanatory model to estimate the development effort based on data of known software projects. It is a serial multiple classifier system based-on several decision trees. The model performance was evaluated by an internal validation procedure, analyzing their robustness and predictive performance. This procedure demonstrates that EEpred is able to estimate the software development effort with a 71 percent of precision. The main advantage of EEpred, regarding to other algorithms, is its ability to translate the process into a collection of simple decision rules, providing more easily interpretable knowledge that can help software engineer to improve decision-making on development planning.

The software cost estimation aims to predict the most realistic effort that is required to finish a software project and so it is critical to the success of a software project management. A Software Cost Estimation affects nearly all management activities, including project bidding, resource allocation and project planning. It is affected by a number of factors, such as implementation efficiency, as well as how much the various reviews and studies completed prior to the software development stage cost. Accurate cost estimation will help us to complete the project on time and within budget. Accurate estimation is important because it has led to extensive research into the methods of software cost estimation. Some important software cost estimation methods have been studied in this research work. In addition, we have set out own criteria, which has been used to compare all the different selected methods. We have also given a score for each evaluation criteria, so that we can compare the different methods numerically for cost estimation. Our observations have shown that it is best to use a number of different estimating techniques or cost models, and then compare the results before determining the reasons for any of the large variations. None of the methods are necessarily better or worse than the others. We found, in fact, that their strengths and weaknesses often complement each other. Therefore, the main conclusion is that there is no one single technique that is best for every situation, and the results of a number of different approaches need to be carefully considered to discover what is the most likely to produce estimates that are realistic.

A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach

The development of a successfull software depends upon the accurate estimation, as various factors are responsible for the overall assessment of any project. The Sofware Cost Estimation is the most complicated and challenging task in software industry. Many estimation models are introduced by the time, that concludes estimation is not a precise science and demanding of new methodologies should be proposed day by day. Software Cost Estimation of a project is vital to the acceptance or rejection of the development of software project. Various techniques have been introduced. This paper enlightens two categories of Software Cost Estimation techniques ie Algorithimic and Non Algorithimic. This paper evaluates the Algorithmic Models such as SLIM, COCOMO, Function Points and Non Algorithimic Models such as Analogy, Expert Judgement which are used to estimate software costs. Poor planning frequently leads to project failure and considerable outcomes for the project team. Software project managers should be aware of the increasing of project failures. In this paper, several existing methods for software cost estimation are illustrated and their aspects will be discussed.

Conclusion instability is the absence of observing the same effect under varying experimental conditions. Deep Neural Network (DNN) and ElasticNet software effort estimation (SEE) models were applied to two SEE datasets with the view of resolving the conclusion instability issue and assessing the suitability of ElasticNet as a viable SEE benchmark model. Results were mixed as both model types attain conclusion stability for the Kitchenham dataset whilst conclusion instability existed in the Desharnais dataset. ElasticNet was outperformed by DNN and as such it is not recommended to be used as a SEE benchmark model.

Medical errors are considered as the leading cause of death and injury. Breast cancer becomes one of the leading causes of death among women, not only in the Philippines but worldwide. In this paper, data mining was used to predict the stage of breast cancer using a hybrid of fuzzy logic and decision tree. This aims to help experts to make decisions rather than replacing them. The result will only give an expert a recommendation, but the final decision is still on the hands of the experts. Feature selection was used to determine the best attribute in the dataset from Surveillance Epidemiology and End Results (SEER). The data set consists of incidence from 1975 to 2016, but the study limits the analysis from 2010 to 2016. Different cleaning and preprocessing of data are conducted. After thorough preprocessing of data, six (6) attributes are selected, and one (1) target class. Performance comparison shows that the fuzzy decision tree achieved a higher accuracy of 99.96%, sensitivity of 99.26% and specificity of 99.98% than the decision tree classification technique. The simulation result shows a correctly classified instance of 165,124, which is equivalent to 99.97% and only 351 incorrect classified instances or 0.21%. Thus, a fuzzy decision tree is more robust than the traditional decision tree classifier for predicting the stage of breast cancer.

One of the most tools of data mining techniques is decision trees for both learning and reasoning from the crisp dataset. In a case of fuzzy dataset, the fuzzy decision tree must be established to extracted fuzzy rules. The paper illustrates an approach to establish fuzzy type-I decision tree from fuzzy type-II dataset using the ambiguity measure in fuzzy type-II form.

The inductive learning methodology known as decision trees, concerns the ability to classify objects based on their attributes values, using a tree like structure from which decision rules can be accrued. In this article, a description of decision trees is given, with the main emphasis on their operation in a fuzzy environment. A first reference to decision trees is made in Hunt et al. (1966), who proposed the Concept learning system to construct a decision tree that attempts to minimize the score of classifying chess endgames. The example problem concerning chess offers early evidence supporting the view that decision trees are closely associated with artificial intelligence (AI). It is over ten years later that Quinlan (1979) developed the early work on decision trees, to introduced the Interactive Dichotomizer 3 (ID3). The important feature with their development was the use of an entropy measure to aid the decision tree construction process (using again the chess game as the considered problem). It is ID3, and techniques like it, that defines the hierarchical structure commonly associated with decision trees, see for example the recent theoretical and application studies of Pal and Chakraborty (2001), Bhatt and Gopal (2005) and Armand et al. (2007). Moreover, starting from an identified root node, paths are constructed down to leaf nodes, where the attributes associated with the intermediate nodes are identified through the use of an entropy measure to preferentially gauge the classification certainty down that path. Each path down to a leaf node forms an ‘if .. then ..’ decision rule used to classify the objects. The introduction of fuzzy set theory in Zadeh (1965), offered a general methodology that allows notions of vagueness and imprecision to be considered. Moreover, Zadeh’s work allowed the possibility for previously defined techniques to be considered with a fuzzy environment. It was over ten years later that the area of decision trees benefited from this fuzzy environment opportunity (see Chang and Pavlidis, 1977). Since then there has been a steady stream of research studies that have developed or applied fuzzy decision trees (FDTs) (see recently for example Li et al., 2006 and Wang et al., 2007). The expectations that come with the utilisation of FDTs are succinctly stated by Li et al. (2006, p. 655); “Decision trees based on fuzzy set theory combines the advantages of good comprehensibility of decision trees and the ability of fuzzy representation to deal with inexact and uncertain information.” Chiang and Hsu (2002) highlight that decision trees has been successfully applied to problems in artificial intelligence, pattern recognition and statistics. They go onto outline a positive development the FDTs offer, namely that it is better placed to have an estimate of the degree that an object is associated with each class, often desirable in areas like medical diagnosis (see Quinlan (1987) for the alternative view with respect to crisp decision trees). The remains of this article look in more details at FDTs, including a tutorial example showing the rudiments of how an FDT can be constructed.

The role of decision trees in software development effort estimation (SDEE) has received increased attention across several disciplines in recent years thanks to their power of predicting, their ease of use, and understanding. Furthermore, there are a large number of published studies that investigated the use of a decision tree (DT) techniques in SDEE. Nevertheless, in reviewing the literature, a systematic literature review (SLR) that assesses the evidence stated on DT techniques is still lacking. The main issues addressed in this paper have been divided into five parts: prediction accuracy, performance comparison, suitable conditions of prediction, the effect of the methods employed in association with DT techniques, and DT tools. To carry out this SLR, we performed an automatic search over five digital libraries for studies published between 1985 and 2019. In general, the results of this SLR revealed that most DT methods outperform many techniques and show an improvement in accuracy when combined with association rules (AR), fuzzy logic (FL), and bagging. Additionally, it has been observed a limited use of DT tools: it is therefore suggested for researchers to develop more DT tools to promote the industrial utilization of DT amongst professionals.

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Estimation of Software Development Effort from Requirements Based Complexity

An effective method for finding the relevant number (window size) and the elapsed time (window age) of recently completed projects has proven elusive in software effort estimation. Although these two parameters significantly affect the prediction accuracy, there is no effective method to stratify and sample chronological projects to improve prediction performance of software effort estimation models. Exemplary projects (Bellwether) representing the training set have been empirically validated to improve the prediction accuracy in the domain of software defect prediction. However, the concept of Bellwether and its effect have not been empirically proven in software effort estimation as a method of selecting exemplary/relevant projects with defined window size and age. In view of this, we introduce a novel method for selecting relevant and recently completed projects referred to as Bellwether moving window for improving the software effort prediction accuracy. We first sort and cluster a pool of N projects and apply statistical stratification based on Markov chain modeling to select the Bellwether moving window. We evaluate the proposed approach using the baseline Automatically Transformed Linear Model on the ISBSG dataset. Results show that (1) Bellwether effect exist in software effort estimation dataset, (2) the Bellwether moving window with a window size of 82 to 84 projects and window age of 1.5 to 2 years resulted in an improved prediction accuracy than the traditional approach.

Software development effort estimation (SDEE) generally involves leveraging the information about the effort spent in developing similar software in the past. Most organizations do not have access to sufficient and reliable forms of such data from past projects. As such, the existing SDEE methods suffer from low usage and accuracy. We propose an efficient SDEE method for open source software, which provides accurate and fast effort estimates. The significant contributions of our article are (i) novel SDEE software metrics derived from developer activity information of various software repositories, (ii) an SDEE dataset comprising the SDEE metrics’ values derived from approximately 13,000 GitHub repositories from 150 different software categories, and (iii) an effort estimation tool based on SDEE metrics and a software description similarity model . Our software description similarity model is basically a machine learning model trained using the PVA on the software product descriptions of GitHub repositories. Given the software description of a newly envisioned software, our tool yields an effort estimate for developing it. Our method achieves the highest standardized accuracy score of 87.26% (with Cliff’s δ = 0.88 at 99.999% confidence level) and 42.7% with the automatically transformed linear baseline model. Our software artifacts are available at https://doi.org/10.5281/zenodo.5095723.