Research Group for Applied Software Engineering
Forschungsgruppe für Angewandte Softwaretechnik

Master Seminar:
Applications of Machine Learning in Software Engineering
(WS 14/15)

Professor: Prof. Bernd Brügge, Ph.D.
Instructors: Tobias Roehm, Stefan Nosovic


This seminar focuses on techniques from machine learning and data mining and surveys their application in software engineering.
In particular, the seminar investigates how such techniques aid program comprehension, help to analyze and interpret large amounts of user input, and facilitate the extraction of relevant knowledge from unstructured data.

During the seminar each participant will read a scientific paper and present it to the seminar audience. More specifically, each participant presents a particular machine learning technique and a particular application in software engineering.

Organizational Issues

  • An information meeting will be held on Wed, 2.4.14, at 14:30 h  in room 01.07.058.
  • Please indicate your interest in the seminar by sending an e-mail to Stefan Nosovic containing a CV and brief description of previous ML/ Data mining experience. Application deadline is Sunday, 6. July 2014, 23:59.
  • The main seminar language will be English but report and presentation can also be done in German.
  • Previous experience with Machine Learning and Data Mining is not mandatory but preferred.
  • The seminar presentations will be done in 1-2 days at the end of the semester.

Possible Topics

Topics will be assigned in the first seminar meeting.

Paper Title Authors Used Technique
Summarizing the Content of Large Traces to Facilitate the Understanding of the Behaviour of a Software System Hamou-Lhadj and Lethbridge Statistics
An information retrieval approach to concept location in source code Marcus et al. Latent Semantic Indexing
Design pattern mining enhanced by machine learning Ference et al. Decision trees, neural networks
How Long will it Take to Fix This Bug? Weiß et al. a-kNN
Duplicate Bug Reports Considered Harmful... Really? Bettenburg et al. SVM


Paper Title Authors Used Technique
A Recommender System for Requirements Elicitation in Large-Scale Software Projects Castro-Herrera et al. Collaborative Filtering
Towards an Intelligent Code Search Engine Kim et al. Summarization based on AST information and K-means
On-demand Feature Recommendations Derived from Mining Public Product Descriptions Dumitru et al. Incremental Diffusive Clustering
Classification of Software Behaviors for Failure Detection: A Discriminative Pattern Mining Approach Lo et al. Frequent iterative pattern mining
Discriminative Pattern Mining in Software Fault Detection Di Fatta et al. Frequent pattern mining: FREQT


Grades will be based on the following criteria:

  • Ability to do independent research
  • Oral presentation
  • Written term paper
  • Active participation in all the other presentations (compulsory attendance)