Leveraging meta-heuristic algorithms for effective software fault prediction: a comprehensive study
Z Dang, H Wang - Journal of Engineering and Applied Science, 2024 - Springer
In large-scale software development, the increasing complexity of software products poses a
daunting challenge to maintaining software quality. Given this challenge, software fault …
daunting challenge to maintaining software quality. Given this challenge, software fault …
A Literature Review on Software Defect Prediction: Trends, Methods, and Frameworks
S Jat, G Vaseer - International Journal of Communication …, 2024 - search.proquest.com
Identifying possible problems at an early point in the development lifecycle is one of the
most important things that software defect prediction can do to enhance software quality and …
most important things that software defect prediction can do to enhance software quality and …
Software fault prediction using a differential evolution-based wrapper approach for feature selection
Software fault prediction (SFP) aids in the early sensing of software flaws, hence improving
the quality of software. The prediction process utilizes previously used software …
the quality of software. The prediction process utilizes previously used software …
FPAFS: Feature Selection Using the Flower Pollination Algorithm for Software Fault Detection System
This paper suggests a suitable feature selection (FS) approach FSFPA using flower
pollination algorithm (FPA). It is based on the concept of flower pollination, to choose a set of …
pollination algorithm (FPA). It is based on the concept of flower pollination, to choose a set of …
Efficient Fault Tolerance Methodology in Fanet Using Aco and Ml Techniques
B Abinaya - … Research Journal on Advanced Engineering Hub …, 2024 - irjaeh.com
An innovative approach is presented in this study to enhance the performance of Ant Colony
Optimization (ACO), a type of Bio-Inspired Algorithm (BIA), by integrating machine learning …
Optimization (ACO), a type of Bio-Inspired Algorithm (BIA), by integrating machine learning …