Practise of AI in Software Testing

 

Practice of Artificial Intelligence (AI) in Software Quality Testing

1. Karthikeyan K, Research Scholar ,College of Management, SRM Institute of Science and Technology,SRM Nagar, Kattankulathur, 603203, Kanchipuram, Chennai, TN, India. Email: Abstract

In Software Development Process, software testing is a vital process to maintain the quality of software. This software testing process are getting complicated, when continuous enhancementof  featuresand continuous delivering to end users. users. This work is about to identifying the bottle neck Software Quality Testing and proposing the best solution to the problem using artificial Intelligence technique, Scientific approaches and better system design. This work will bring the  better refinement in the regression testing testing spaces. And solves common progression testing  problem. Keywords: Software Testing, Artificial Intelligence, Quality, System Engineering Introduction: Now a days every organization uses their own software or 3rd party software to run

their day-to-day business. Especially e-commerce organization totally function on the software. Even 10 min crash crash of software software might impact the business and posses the risk risk of losing losing huge money and customer satisfaction. So obvious that software quality plays a vital role in that. Artificial intelligence can improve the performance of software quality process. This study will focus on the below KPIs of software testing coverage gap (Whether we tested all possible scenarios?), reducing the production defect, optimizing the number of test cases, reducing the manual effort and Effective transfer of test cases from progression to regression testing. BlackBox Testing with Info-Fuzzy Networks: Based on the input and output of the software develop a fuzzy network and identify the causes cau ses based on the effort. This is helpful to reduce the coverage gap.Predicting the test cases failures based on the git code commit changes which will improve the efficiency of software testing.

References:

1. Fuzzy Cause Cause - Effect Effect Models Models of Software Software Testing Testing (Witol (Witold d Pedrycz and and George Vukovich )

 

2. Black-Box Black-Box Testing Testing with with Info-Fuzzy Info-Fuzzy Networks Networks (Mark (Mark Last Last and Menahem Menahem Friedman) Friedman) 3. Automated Automated GUI Regression Regression Testin Testing g Using AI AI Planning Planning (AtifM. (AtifM. Memon) Memon) 4. Test Set Generat Generation ion And Reduction Reduction With Artifi Artificial cial Neural Neural Networks Networks (Prachi (Prachi Saraph, Abraham Kandel, and Mark Last) 5. Three-Group Three-Group Softwar Softwaree Quality Quality Classifi Classificati cation on Modeling Modeling Using An An Automated Automated Reasoning Approach (Taghi M. Khoshgoftaar and Naeem Seliya) 6. Data Mining Mining with with Resampling Resampling in Softwar Softwaree Metrics Metrics Databases Databases 175 175 Scott Dick Dick and Abraham Kandel