CodeFault: Analyzing Human Dimensions of Software Engineering Processes

Abstract

We propose CodeFault, a system that automatically gathers software development behavior and issue/vulnerability data from a number of heterogenous sources and then mines that data using machine learning techniques to generate models that help predict a range of software bugs, including potential vulnerabilities. Sources we will gather from include: the code itself (as a document), source control systems, social coding sites, social media sources, discussion forums, and issue repositories. To learn predictors, we will identify raw (or derived) features from the combined set of sources, and employ ensemble-style machine learning techniques to do the learning. By predicting these faults, teams can proactively respond to potential flaws, as well as gradually learn what human behavioral trends that are conducive towards that problem. The result saves not only time and money for organizations, but more importantly helps ensure better security. CodeFault builds on previous technology we have built in both the vulnerability aggregation and code analysis space.

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Document Details

Document Type
Technical Report
Publication Date
Apr 10, 2022
Accession Number
AD1170934

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Bayesian Networks
  • Computer Languages
  • Computer Programming
  • Computer Programs
  • Data Mining
  • Data Sets
  • Databases
  • Deep Learning
  • Engineering
  • Human Behavior
  • Identification
  • Information Science
  • Machine Learning
  • Ontologies
  • Probabilistic Models
  • Risk Analysis
  • Social Media
  • Software Development
  • Statistical Analysis
  • Supervised Machine Learning
  • Vulnerability

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation
  • Cybersecurity.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks
  • Space