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Problem statement: Estimation models in software engineering are used to predict some important attributes of future entities such as development effort, software reliability and programmers productivity. Among these models, those estimating software effort have motivated considerable research in recent years. Approach: In this study we discussed an available work on the effort estimation methods and also proposed a hybrid method for effort estimation process. As an initial approach to hybrid technology, we developed a simple approach to SEE based on use case models: The "use case point's method". This method is not new, but has not become popular although it is easy to understand and implement. We therefore investigated this promising method, which was inspired by function points analysis. Results: Reliable estimates can be calculated by using our method in a short time with the aid of a spreadsheet. Conclusion: We are planning to extend its applicability to estimate risk and benchmarking measures

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Data analysis plays a major role in different research applications that require a large volume of data. Cloud computing can provide computer processing resources and device‐to‐device data sharing based on user requirements. The main goal of cloud computing is to allow users and enterprise of varying capabilities to store and process data in an efficient way and to access and distribute resources. However, a crucial problem in cloud computing is job scheduling for numerous users. Prior to the implementation of job scheduling, jobs must be categorized according to degree of criticalness, privacy and time required. Based on the experimental results, the combination of tasks was successfully determined by the processor. In heterogeneous multiprocessor systems, customized job scheduling is highly critical for obtaining optimal job performance. In this paper, an evolutionary genetic algorithm was used for obtaining better results in job scheduling, thereby improving performance in the cloud system in this regard. The genetic algorithm‐based job scheduling process introduced minimizes the investment in time through effective allocation of user requests in order to enhance the overall efficiency of the system.

Knowledge management along with data analytic process provides the collection of information which helps to examine both qualitative and quantitative software information. During the knowledge based data examination process, software effort estimation (SEE) is an organised approach to increase the efficiency of a software development process in various organisations. Software effort estimation based on use case point (UCP) methods provides only fixed estimation value which cannot deal with the uncertain and ambiguous conditions of the particular software. This work attempts to provide a fuzzy effort estimation procedure for use case model based on fuzzy inference rules. This paper also proposes a metric to calculate the enhanced UCPs with the fuzzy membership function for examining the software distinguishing quality. The proposed approach has been validated against the goal-driven UCP model, tree-boost model, Regression model and traditional use-case model. The obtained results show the better performance than those obtained by the existing methods.