Abstract Effort-Aware Defect Prediction (EADP) technique sorts software modules by the
defect density and aims to find more bugs when testing a certain number of Lines of Code …

As software systems undergo escalating complexity, the identification of bugs and defects
becomes pivotal for ensuring seamless user experiences and averting potentially costly post …

As software products become larger and more complex, the test infrastructure needed for
quality assurance grows similarly, causing a constant increase in operational and …

This experience paper describes thirteen considerations for implementing machine learning
software defect prediction (ML SDP) in vivo. Specifically, we provide the following report on …

This work integrates logical and physical security processes, and simplifies the
manageability of the security infrastructure. The process increases visibility to resources …

Software stands out as one of the most rapidly evolving technologies in the present era,
characterized by its swift expansion in both scale and complexity, which leads to challenges …

This study explores machine learning (ML) techniques for Software defects prediction (SDP)
by using Mathematical Modelling & Simulation. The SDP is also used in the critical systems …

This empirical investigation delves into the influence of machine learning (ML) algorithms in
the realm of cross-project defect prediction, employing the AEEEEM dataset as a foundation …

Quality of large-scale mission-critical software systems depends on software system
architecture. Although we design and create software system architecture we are still unable …