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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Amit Verma
Chandigarh University
Computer Science & IT Engineering

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Design an Algorithm for Software Development in Cbse Environment using Feed Forward Neural Network Amit Verma; Pardeep kaur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 2: June 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (575.229 KB) | DOI: 10.11591/ijai.v4.i2.pp53-61

Abstract

A In software development organizations, Component based Software engineering (CBSE) is emerging paradigm for software development and gained wide acceptance as it often results in increase quality of software product within development time and budget. In component reusability, main challenges are the right component identification from large repositories at right time. The major objective of this work is to provide efficient algorithm for storage and effective retrieval of components using neural network and parameters based on user choice through clustering. This research paper aims to propose an algorithm that provides error free and automatic process (for retrieval of the components) while reuse of the component. In this algorithm, keywords (or components) are extracted from software document, after by applying k mean clustering algorithm. Then weights assigned to those keywords based on their frequency and after assigning weights, ANN predicts whether correct weight is assigned to keywords (or components) or not, otherwise it back propagates in to initial step (re-assign the weights). In last, store those all keywords into Repositories for effective retrieval. Proposed algorithm is very effective in the error correction and detection with user base choice while choice of component for reusability for efficient retrieval is there. To check the results of our algorithm based on factors like accuracy, precision and recall compare with existing technique i.e. integrated classification scheme for retrieval of components based on keyword search and results are so encouraging.
Design and Development of an Agorithm for Prioritizing the Test Cases Using Neural Network as Classifier Amit Verma; simranjeet kaur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 1: March 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (582.919 KB) | DOI: 10.11591/ijai.v4.i1.pp14-19

Abstract

Test Case Prioritization (TCP) has gained wide spread acceptance as it often results in good quality software free from defects. Due to the increase in rate of faults in software traditional techniques for prioritization results in increased cost and time. Main challenge in TCP is difficulty in manually validate the priorities of different test cases due to large size of test suites and no more emphasis are made to make the TCP process automate. The objective of this paper is to detect the priorities of different test cases using an artificial neural network which helps to predict the correct priorities with the help of back propagation algorithm. In our proposed work one such method is implemented in which priorities are assigned to different test cases based on their frequency. After assigning the priorities ANN predicts whether correct priority is assigned to every test case or not otherwise it generates the interrupt when wrong priority is assigned. In order to classify the different priority test cases classifiers are used. Proposed algorithm is very effective as it reduces the complexity with robust efficiency and makes the process automated to prioritize the test cases.
Co-Authors Pardeep kaur simranjeet kaur