Evaluating Machine Intelligence Techniques for Software Effort Estimation with Failure Patterns and Time Constraints

Abstract

Software effort estimation plays a critical role in project management, especially in the face of failure patterns and time constraints that complicate predictive accuracy. This study evaluates the application of machine intelligence techniques, specifically Hierarchical Temporal Memory (HTM) and Auditory Machine Intelligence (AMI), to improve estimation accuracy. Employing a quantitative methodology, the research investigates five core aspects: comparative effectiveness of HTM and AMI, the impact of failure patterns on estimation accuracy, the influence of time constraints on technique performance, complexity trade-offs, and optimization strategies for real-time applications.
Findings reveal HTM's superior accuracy compared to AMI, though its complexity poses challenges for real-time usability. Failure patterns significantly influence estimation accuracy, emphasizing the need for adaptive models. Time constraints were found to affect the performance of both techniques, with HTM displaying greater sensitivity. Optimization techniques demonstrated the potential to mitigate complexity and enhance real-time applicability.
This research contributes to the evolving field of software effort estimation by providing a nuanced understanding of the interplay between machine intelligence, failure patterns, and time dynamics. While limitations include reliance on historical datasets and restricted real-time validation, future research can address these gaps by exploring diverse datasets and advanced optimization techniques, offering practical implications for more accurate and efficient software project planning.