The goal of the course is to analyze large amounts of software engineering data using data mining techniques to uncover interesting and actionable information about software systems and projects. We use modern tools and techniques for mining this data in order to discuss the associated challenges and outline future research directions.
Examples include bug prediction using classifiers, search-based software engineering, and pattern mining of git repositories.
In the final lectures, we study recent research to understand how the mining of software repositories is evolving. For these lectures, the students will prepare a presentation on which they will be graded.
The goal of the practical sessions is to apply and to extend state-of-the art methodologies and tools to real software projects. Students will be graded on three assignments in which they will extend state-of-the-art frameworks.
|Introduction to the Course||
How to Read an Engineering Research Paper
Future of Mining Software Archives: A Roundtable
|Bug Prediction: Product Metrics||
Evaluating defect prediction approaches: a benchmark and an extensive comparison
|Bug Prediction: Process and Developer-based Metrics||A Developer Centered Bug Prediction Model||process metrics.pdf|
|Bug Prediction: Automatic Identification of Bug-Introducing Changes||
When do Changes Induce Fix?
Automatic Identification of Bug-Introducing Changes
|Bug Prediction: The Choice of the Classifier||Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models|
|Bug Prediction: Ensembles of Classifiers||
Software defect prediction: do different classifiers find the same defects?
Dynamic selection of classifiers in bug prediction: An adaptive method
|Bug Prediction: Cross-Project and Just-In-Time Bug Prediction||
Cross-project Defect Prediction A Large Scale Experiment on Data vs. Domain vs. Process
A Large-Scale Empirical Study of Just-in-Time Quality Assurance
|Search Based Software Engineering: Introduction||
Search Based Software Engineering: Techniques, Taxonomy, Tutorial
Achievements, open problems and challenges for search based software testing
|Search Based Software Engineering: Using Genetic Algorithm to Configure Machine Learning Techniques||
A Genetic Algorithm to Configure Support Vector Machines for Predicting Fault-Prone Components
Data-Driven Search-based Software Engineering
|Bug Prediction: Dealing with Data Quality|
|Pattern Mining: Mining Code Idioms||
Treefinder: a first step towards XML data mining.
Mining Idioms from Source Code
|Pattern Mining: Toward Deep Learning Software Repositories||Toward Deep Learning Software Repositories|
|Pattern Mining: Are Deep Neural Networks the Best Choice for Modeling Source Code?||
Are Deep Neural Networks the Best Choice for Modeling Source Code?
Easy over Hard - A Case Study on Deep Learning
There is no traditional oral or written exam. Students will be graded as follows:
Note that failing to hand in an assignment or failing to present automatically results in an ABSENT mark.
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