Efficient and Noiseless Mining of Software Engineering Data with Optimized Effort using TLBO: A Novel Approach

Abstract: The domain of software engineering has been emerging as a challenging field in other domains of expertise. With the rapid evolution and the presence of software based tools have proved the applicability of data mining in the field of software engineering. This field is linked with the application of data mining methods to offer valuable insights into how to advance software engineering processes and software itself, supporting decision-making. The main rationale here is to convey the role of software engineering as a method so as to grab the attention of our community that can prove as an attractive leeway for data mining applications and to show how data mining can considerably add to software engineering research. Utilizing entrenched information mining strategies, professionals and analysts can investigate the capability of this important information keeping in mind the end goal to better deal with their activities and to create higher-quality programming frameworks that are conveyed on time and within spending plan or budget. This paper offer an approach that how data mining can make a noteworthy involvement to the success of current software engineering efforts and will tend to provide some set of recommendations that is meant to increase the success of experts participation and model satisfactoriness

Keywords: Software Mining, Teaching Learning Based Optimization, MMRE, Artifacts.