Effort estimation of software development is an important sub-discipline in software engineering. It has been the focus of much research mostly over the last couple of decades. In recent years, software development turned into engineering through the introduction of component-based software development (CBSD). The industry has reported significant advantages in using CBSD over traditional software development paradigms. However, the introduction of CBSD has also brought a host of unique challenges to software effort estimation which are quite different from those associated with traditional software development. Owing to the increasing tendency to use the CBSD approach in recent years, it is clear that effort estimation of CBSD is particularly an important area of research with a direct relevance to industry. In this study, the authors survey the most up-to-date research work published on predicting the effort of CBSD. The authors analyse the surveyed approaches in terms of modelling technique, the type of data required for their use, the type of estimation provided, lifecycle activities covered and their level of acceptability with regard to any validation. The aim of this survey is to provide a better understanding of the cost and schedule estimation approaches for CBSD.

Effort estimation of component-based software development – a survey

Software estimations are regarding based on prediction properties of system, with attention to development methodology. In object-oriented analysis, Use Case models describe the functional requirements of a software system, so they can be basis for software measurement and sizing. Use Case points method that suggested by karner, is based on size and complexity of Use Cases. This method earns the great successes in industry and university. Although this method doesn't have enough precision and accuracy, because of inaccuracy in determining Use Case complexity metrics. In this research, we are going to assessment weak points of Use Case complexity metrics specially transaction metric in Use Case points method and then we will introduce other metrics for determining Use Case complexity that it can be led to simplify calculating UUC and it can be also tried to cover the most of aspects of Use Cases.

Software complexity level determination using software effort estimation use case points metrics

Software fault prediction area is subject to problems like non availability of fault data which makes the application of supervised techniques difficult. In such cases unsupervised approaches like clustering are helpful. In this paper, K-Medoids clustering approach has been applied for software fault prediction. To overcome the inherent computational complexity of KMedoids algorithm a data structure called Kd-Tree has been used to identify data agents in the datasets. Partitioning Around Medoids is applied on these data agents and this results in a set of medoids. All the remaining data points are assigned to the nearest medoids thus obtained to get the final clusters. Software fault prediction error analysis results show that our approach outperforms all unsupervised approaches in the case of one given real dataset and gives best values for the evaluation parameters. For other real datasets, our results are comparable to other techniques. Performance evaluation of our technique with other techniques has been done. Results show that our technique reduces the total number of distance calculations drastically since the number of data agents is much less than the number of data points.

Application of K-Medoids with Kd-Tree for Software Fault Prediction

The Use of Cyclomatic Complexity Metrics in Programming Performance's Assessment☆

Software metrics and fault data belonging to a previous software version are used to build the software fault prediction model for the next release of the software. However there are certain cases when previous fault data are not present. In other words predicting the fault-proneness of program modules when the fault labels for modules are unavailable is a challenging task frequently raised in the software industry. There is need to develop some methods to build the software fault prediction model based on supervised learning which can help to predict the fault–proneness of a program modules when fault labels for modules are not present. One of the methods is use of classification techniques. Supervised techniques like classification may be used for fault prediction in software modules, more so in those cases where fault labels are not available. In this study, we propose a Genetic algorithm based software fault prediction approach for classification.

/pdf/predicting-the-software-fault-using-the-method-of-enetic-4x28bopabl.pdf

Predicting the Software Fault Using the Method of enetic Algorithm

The problem of cost estimation in software engineering has been addressed by several researchers. Research shows that among many factors that affect the development cost, size of the product plays an important role. In addition to the size, product properties like complexity, cohesion and coupling are also mentioned as cost factors. We focus on the relationship between complexity of a product to its development cost. The aim of this research is to identify the use of complexity metrics as important parameters in similarity matching for retrieval of cases in case based effort estimation. The objective is to estimate the development effort of student programs based on the values of certain attributes using case based reasoning model. We have developed a case based estimation model using complexity metrics and have validated it upon student data. Our focus is on object oriented programs.

http://www.jatit.org/volumes/research-papers/Vol6No1/0Vol6No1.pdf

Complexity metric for analogy based effort estimation

Accuracy in the estimation of software Effort/Cost is one of the desirable criteria for any software cost estimation model. The estimation of effort or cost before the actual development of any software is the most crucial task of the present day software development project managers. Software project attributes are often measured in terms of linguistic values such as very low, low, Average, high and very high. The imprecise nature of such attributes constitutes uncertainty and vagueness in their subsequent interpretation. In this paper we propose a Fuzzy logic based model for software effort prediction. We feel that fuzzy Software cost estimation Model should be able to deal with imprecision and uncertainty associated with various parameter values. Fuzzy analogy model has been developed and validated upon student data.

/pdf/software-effort-prediction-a-fuzzy-logic-approach-4g4elj4ltn.pdf

Software Effort Prediction - A Fuzzy Logic Approach

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Complexity metric for analogy based effort estimation

Software Effort Prediction - A Fuzzy Logic Approach