Extracting Knowledge From Unstructured Data -
Applications of Machine Learning in Software Engineering
Master Seminar (IN2107, IN8901)
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 present a machine learning technique including a particular application in software engineering.
- The presentations will take place from 12:30 to 16:30 on:
- Thu, February 2 in room 01.07.058
- Fri, February 3 in room 01.07.014
- In case of any questions please write an e-mail to pagano (at) in.tum.de.
|Thursday, February 2, 2012 - room 01.07.058
||Summarizing the Content of Large Traces to Facilitate the Understanding of the Behaviour of a Software System
||Hamou-Lhadj and Lethbridge
||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.
||Miguel Fernando Cabrera
||Duplicate Bug Reports Considered Harmful... Really?
||Bettenburg et al.
|Friday, February 3, 2012 - room 01.07.014
||A Recommender System for Requirements Elicitation in Large-Scale Software Projects
||Castro-Herrera et al.
||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)