MANIKKANNAN

@srmist.edu.in

Assistant Professor, Engineering & Technology
Assistant Professor, Online Education
SRM Institute of Science and Technology, Kattankulathur

MANIKKANNAN


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https://researchid.co/manikkannan

EDUCATION

Ph.D. in Computer Science and Engineering

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Software, Computer Vision and Pattern Recognition
4

Scopus Publications

Scopus Publications

  • Test Case Prioritization via Embedded Autoencoder Model for Software Quality Assurance
    D. Manikkannan, S. Babu
    IETE Journal of Research, 2024
    Software quality can be ensured by passing the process of software testing before the software is released. However, the software testing process involves many phases which leads to more resources and time consumption. Test Case Prioritization (TCP) has gained widespread acceptance because it prioritizes the tasks and reduces the number of phases and also produces results in good quality software free from defects. The coverage-based prioritization can be useful to distinguish each test case and result in a better prioritization process by using some algorithm. In this work, we propose a coverage-based prioritized test case generation using an Embedded Auto Encoder (EAE) algorithm which will produce an ordered sequence of the prioritized test cases. Initially, the code coverage for each benchmark has been extracted from the source code repository and is further processed to eliminate the noise in the data. The processed data will be given to the embedded autoencoder which consists of an autoencoder and a sparse autoencoder. Once the modeling is done, the model has been trained with the data generated by the Keras Data Generator class. The efficiency of the proposed EAE technique has been evaluated by using the APFD metric and the observations clearly show that the EAE framework proves to provide an APFD metric of 0.72 on average which is a good value in comparison to the previously deployed methodologies.
  • Academic Degree Verification Using Blockchain Technology
    M. S. Achyut, K. Sabarish, H. Mohsin Ahmed, D. Manikkannan
    Lecture Notes in Networks and Systems, 2024
  • Automating Software Testing with Multi-Layer Perceptron (MLP): Leveraging Historical Data for Efficient Test Case Generation and Execution
    International Journal of Intelligent Systems and Applications in Engineering, 2023
  • Software Bugs Detection Using Supervised Machine Learning Techniques
    Manthan Shah, Ayushi Sharma, Rizon Kumar
    Advances in Science and Technology, 2023
    A software bug is some sort of a fault in the source code or a computer program. These bugs work in unusual and unintended ways which is a serious problem for a programmer and the company. Detecting bugs in software has been tried and tested through multiple means, the most recent of which is Machine Learning algorithms. Using a revolutionary dataset consisting of real software code snippets of the C language into key values, we were able to train the algorithms based on numerical parameters. This in turn simplified our algorithmic process. Furthermore, we run multiple classification algorithms to gain precision, recall and Fn scores, and improve upon these scores using key hyperparameter tuning techniques. Our observations revealed an increase in accuracy and were able to create an end module which can directly take the source code as an input from which the metrics and features are extracted and give the output if the code has a software bug or not.