• Ketna Khanna
• Naresh Chauhan
• Vedpal

Software Testing is immensely important task because thorough testing improves the quality of the software and reduces its maintenance cost. This work proposes a novel way of Black Box Testing that uses Rate of Fault Detection and Genetic Algorithms (GA) for the set task. GA is known for finding near optimal solution in very large search space based on the fitness function. Here fitness function has been crafted using rate of fault detection which is a measure that calculates the detection of as many faults as possible in the least amount of time i.e. as quickly as possible. Model has been explained and the results are demonstrated. Comparison of the proposed work with the other existing approach is performed using APFD metric. The work gives encouraging results and would be extending by using various data sets and applying other optimization techniques in future.

Black Box Testing, Genetic Algorithms, Test Case Prioritization

"Genetic Algorithms based Black Box Testing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.537-544, November-2018, Available :http://www.jetir.org/papers/JETIR1811A65.pdf

"Genetic Algorithms based Black Box Testing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp537-544, November-2018, Available at : http://www.jetir.org/papers/JETIR1811A65.pdf