Applications of Machine Learning in Software Engineering
Master Seminar (IN2107, IN8901)
Beschreibung
Thema des Seminars sind Ansätze aus den Bereichen Maschinelles Lernen und Data Mining und deren Einsatz in der Softwareentwicklung. Es wird diskutiert, wie solche Ansätze eingesetzt werden können, um Programme besser zu verstehen, große Mengen an Benutzerinput zu analysieren und wie Wissen und Zusammenhänge aus unstrukturierten Daten gewonnen werden kann.
Jeder Teilnehmer präsentiert dabei eine Ansatz des Maschinellen Lernens und eine Anwendung in der Software Entwicklung.
Summary
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.
Organizational Issues
- NEW NEW NEW The kickoff meeting will be on 6.11, 14-15 h, room 01.07.58. We will discuss research methods and seminar topics in that session. NEW NEW NEW
- Report and presentation can be in German or English.
- For signing up in the seminar write an email to emitza.guzman(at)mytum.de.
- In case of any questions please write an e-mail to emitza.guzman(at)mytum.de.
Modalities
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)
Lecturers
Teaching Assistants
Topics
Paper |
Conf. |
ML Technique |
Presenter |
Advisor |
Henß et al.: |
ICSE 2012 |
Latent Dirichlet Allocation |
Aleksandar Stoimenov |
Emitzá Guzmán |
Bacchelli et al.: |
ICSE 2012 |
Naive Bayes |
Martin Stoll |
Emitzá Guzmán |
McMillan et al: |
ICSE 2012 |
Latent Semantic Indexing |
Hao Ji |
Emitzá Guzmán |
Murphy-Hill et al.: |
FSE 2012 |
Collaborative Filtering |
Burak Söhmelioglu |
Emitzá Guzmán |
Zhong et al.: |
ASE 2009 |
Hidden Markov Chains |
Viktor Bogishef |
Emitzá Guzmán |
Kim et al.: |
AAAI 2010 |
K-means |
Pei Li |
Emitzá Guzmán |
Di Fatta et al.: |
SOQUA 2006 |
Frequent subtree mining |
Mads Esholdt Wiemann |
Tobias Röhm |
Lo et al.: |
KDD 2009 |
Frequent iterative pattern mining, classification |
Vesko Georgiev |
Tobias Röhm |
Ghazarian, Noorhosseini: Automatic detection of users' skill levels using high-frequency user interface events |
User Modeling and User-Adapted Interaction, 2010 |
Decision Trees |
Rui Yuan |
Tobias Röhm |
Tseng, Lin: Efficient mining and prediction of user behavior patterns in mobile web systems |
Information and Software Technology 2006 |
SPAM mine (own algo) |
Serge Krauze |
Tobias Röhm |
Saito et al.: Understanding user behavior through summarization of window transition logs |
Databases in Networked Information Systems, 2011 |
HMM (task summary) |
Zdravko Georgiev |
Tobias Röhm |
Romero et al.: |
ICSM, 2005 |
Machine Learning techniques |
Mehdi Foudhaili |
Tobias Röhm |