How You Can Improve The Accuracy of Software Development Effort Estimations

How You Can Improve The Accuracy of Software Development Effort Estimations

The write-up discusses in detail the positive of software development process. It also discusses the methods using which software development accuracy estimations can be improved for better results. It further elaborates the positives of using these methods and what makes them a success.

By Vikas Kumar Sinha

The software industry is booming with every passing day. More & more innovative software are coming to the scene for relieving businesses from the trouble of handling the complexities in their work environment. Needless to mention that a massive number of software developers in India & abroad are constantly working for developing such software. Surely, the efforts of these qualified professionals located across the globe for serving the industry with quality software are appreciable.

In regard to the success of any software project, the input effort plays a major role in deciding the competency to meet the project requirements. Hence, its becomes quite essential to have an accurate measure of the input software development effort. For software development projects of any size & complexity, one of the most crucial metrics that should be accurately estimated is software development effort. Basically following are the two methods using which software development effort accuracy estimation can be improved -

Use of Artificial Neural Network Model

Prominent factors that decide the overall success rate of a software project are time, cost, and manpower. Its during the planning phase that all of these factors are given due consideration by the project managers. After evaluation of mentioned factors, the project manager can rest assured of reaping favorable outcomes from the project that includes better project completion efficiency.

For better software development effort accuracy  estimation, the Artificial Neural Network Model (ANN) is incorporated with the Constructive Cost Model (COCOMO); which is further optimized using Particle Swarm Optimization (PSO). This modified model allows for following -

  • Increasing the artificial neural network convergence speed
  • Improving the original model's learning ability while offering the pros of COCOMO model

Use of Clustering

Artificial Neural Networks (ANN)  and Analogy-based estimation (ABE) are the two most popular methods for accurate estimation of software development effort. Irrelevant & non-uniform projects exiting in the software project data-sets lay a significant impact on both of these methods. To have better accuracy, there are also hybrid methods generated by combining ANN and ABE together. As per the proposed method, a new framework is designed for countering the effects of  irrelevant & non-uniform projects on these methods.

Another significant advantage of the proposed method is the improvements gained in ANN training quality and ABE historical data consistency. In order to evaluate the proposed method's performance,  two real data sets are made use of; the results so obtained are compared relative to eight other estimation methods. The proposed method has come out to be a highly effective method and outperforms all other methods.

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Author: Vikas Kumar Sinha

The author has produced & edited several articles and related informative material for a range of genres including web design & development, software development, Internet marketing etc.  He likes to create informative content to educate readers regarding the current tech trends while remaining engaged in a host of online promotional activities for his organization.

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