Gaddala Lalitha Kumari

Work place: Dept. of CSE, PVP Siddhartha Institute of Institute of Technology, Vijayawada 520007, India

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Research Interests: Artificial Intelligence, Autonomic Computing, Computational Learning Theory, Data Mining, Data Structures and Algorithms

Biography

Dr G Lalitha Kumari is a leading Researcher & Academician in Computer Science & Engineering and holds Ph.D. degree. She is currently working as Sr Assistant Professor in the Dept of Computer Science & Engineering at Prasad V Potluri Siddhartha Institute of Technology (A), Andhra Pradesh, India.. 15 Years of Teaching experience. She has Journal Publications: 22(6-SCOPUS+1(Free Scopus), UGC-5, Others-10) and International Conferences – 4 | Patents: 02 published and 01(Granted). Her Research interests include, Soft Computing, Data Mining, , Data Science, Artificial Intelligence and Machine Learning, Deep Learning.

Author Articles
Prioritization of Test Cases in Software Testing Using M2 H2 Optimization

By Kodepogu Koteswara Rao M Babu Rao Chaduvula Kavitha Gaddala Lalitha Kumari Yalamanchili Surekha

DOI: https://doi.org/10.5815/ijmecs.2022.05.06, Pub. Date: 8 Oct. 2022

By and large, software testing can be well thought-out as a adept technique of achieving improved software quality as well as reliability. On the other hand, the eminence of the test cases had significant effect on the fault enlightening competence of testing activity. Prioritization of Test case (PTC) remnants one challenging issue, as prioritizing test cases remains not up in the direction of abrasion by means of respect to Faults Detected Average Percentage (FDAP) and time execution results. The PTC is predominantly anticipated to scheme assortment of test cases in accomplishing timely optimization by means of preferred properties. Earlier readings have been presented for place in order the accessible test cases in upsurge speed the fault uncovering rate in testing. In this phase, this learning schemes a Modern modified Harris Hawks Optimization centered PTC (M2H2O-PTC) method for testing. The anticipated M2H2O-PTC method aims to exhaust the possibilities the FDAP and curtail the complete execution time. Besides, the M2H2O algorithm is considered for boosting the examination and taking advantage abilities of the conservative H2O algorithm. For validating the enhanced efficiency of the M2H2O-PTC method, an extensive variety of simulations occur on contradictory standard programs and the outcomes are inspected underneath numerous characteristics. The investigational results emphasized enhanced proficiency of the M2H2O-PTC method in excess of the modern methodologies in standings of dissimilar measures.

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