This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Today's society has grappled with the age of big data. Widespread use of informatization technology has promoted the development of artificial intelligence and communication technologies, which can play an important role in communication networks. In this paper, an energy-saving mechanism based on genetic algorithm in wireless sensor network (WSN) is proposed. The basic working principle and main characteristics of genetic algorithm (GA) are summarized, and the theory, technology and existing problems of GA are discussed. Through the analysis of the transmission efficiency of GA, a new genetic evolutionary algorithm combined with the characteristics of ant colony algorithm (ACO) is proposed. Through the simulation of the transmission performance of genetic optimization algorithm, the comparison of transmission energy consumption between GA and evolutionary algorithm is analyzed, and the evolutionary algorithm with higher transmission performance is obtained. Results showed that the proposed hybrid genetic algorithm with ant colony optimization (GACO) delivers 78.70% and 73.51% lower number of transmission failures than GA and ACO respectively.

Efficient Transmission based on Genetic Evolutionary Algorithm

Resource allocation and tasks assignment to software development teams are very crucial and arduous activities that can affect a project's cost and completion time. Solution for such problem is NP-Hard and requires software managers to be supported with efficient tools that can perform such allocation and can resolve the software development project scheduling problem (SDPSP) more efficiently. Ant colony optimization (ACO) is a rapidly evolving meta-heuristic technique based on the real life behavior of ants and can be used to solve NP-Hard (SDPSP) problem. Different versions of ACO meta-heuristic have already been applied to the software project scheduling problem in the past that took various resources into account. We have applied elitist strategy of ACO (elitist ant system) for solving SDPSP in a parameter-constrained environment taking project's cost and duration into consideration. The objective of the ACOSDPSP methodology allows software project managers and schedulers to assign most effective set of employees that can contribute in minimizing cost and duration of the software project. Experimental results show that the proposed ACOSDPSP methodology is promising in achieving the desired results. General Terms Simulation,Data dictionary/directory ,Data warehouse and repository,Logging recovery,Security,integrity and protection

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Divyansh Singh

Papers

Survey on Project Management System using Event based Scheduler and Ant Colony Optimization