Identifying Software Defect Density in the Aerospace Industry Using Cross-Project Metrics and Imbalanced Learning

Software defect prediction (SDP) plays a pivotal role in enabling predictive quality assurance during the early phases of the aerospace software development lifecycle. The cost to identify and resolve defects rises significantly throughout the lifecycle, which can consume 50% of the total development budget. This can be avoided if defects are detected and resolved early.Aerospace software consists of highly complex and interdependent modules that directly impact the overall project quality which may result in a mission failure. Early defect data can provide quality assurance teams the ability to optimally allocate labor resources to both improve software reliability and reduce development costs. The assemblage of past aerospace software product metric data provides historical defect details used to distinguish the difference between defective modules and non-defective modules.Current defect prediction models face several challenges such as inadequate treatment of software module class imbalances, data heterogeneity, multicollinearity, and the curse of dimensionality. Predictive model deficiencies can lead to substandard and unreliable defect identification performance. These current predictive model shortfalls motivate the need to improve automatically identifying software defect densities in future software products. This implementation can provide optimal insight on defective data to quality assurance and engineering teams early in the software development lifecycle.This research introduces a novel approach to predicting defective aerospace software module densities and providing an alternative to the resource-intensive task of obtaining semi-labeled metric module data from project source files. The proposed cross-project defect prediction model effectively identifies the defect densities of aerospace software products based on several evaluation metrics by using static cross-project metrics. This research establishes a benchmark for the aerospace industry to measure software quality and reliably identify defective software modules in the early stages of the software development lifecycle.

Author

• Lachhander, Sean Kishan

Language

• en

Keyword

• machine learning
• aerospace
• predictive model
• imbalanced learning
• software defects
• defect density

Date created

• 2022

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Yassan, Rebecca
• Blackford, Joseph P

Committee Member(s)

• Etemadi, Amir

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/4f16c358w