Inferring Causality with Data from Personal Software Process

Abstract

Can Causal Algorithms Help? To control software development, we need factors that 1) Can be selected or manipulated 2) Have a causal effect (direct or indirect) on desired outcomes. New algorithms and techniques are becoming available. They are related to but distinct from multiple regression, Bayesian networks, and Machine Learning. The methods have been successful in other domains. How can we gain confidence when applying to Software Engineering?

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2018
Accession Number
AD1086994

Entities

People

  • Michael Konrad
  • William R. Nichols

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Bayesian Networks
  • Copyrights
  • Cost Overruns
  • Costs
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Engineering
  • Governments
  • Machine Learning
  • Materials
  • Measurement
  • Predictive Modeling
  • Risk Management
  • Software Development
  • Students
  • Universities

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks