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Mit 1.10.2022 ist die Fakultät für Physik in der TUM School of Natural Sciences mit der Webseite https://www.nat.tum.de/ aufgegangen. Unter Umstellung der bisherigen Webauftritte finden Sie weitere Informationen.

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Praktikum: Machine Learning for Software Testing (IN0012; IN2106, IN4227)
Practical: Machine Learning for Software Testing (IN0012; IN2106, IN4227)

Lehrveranstaltung 0000003134 im WS 2017/8

Basisdaten

LV-Art Praktikum
Umfang 6 SWS
betreuende Organisation Informatik 4 - Lehrstuhl für Software & Systems Engineering (Prof. Pretschner)
Dozent(inn)en Leitung/Koordination: Alexander Pretschner
Mitwirkende:
Mojdeh Golagha
Termine

Zuordnung zu Modulen

weitere Informationen

Lehrveranstaltungen sind neben Prüfungen Bausteine von Modulen. Beachten Sie daher, dass Sie Informationen zu den Lehrinhalten und insbesondere zu Prüfungs- und Studienleistungen in der Regel nur auf Modulebene erhalten können (siehe Abschnitt "Zuordnung zu Modulen" oben).

ergänzende Hinweise We’re living in a world where we’re surrounded by the services that are adopting more and more features driven by machine learning systems. We are witnessing the emergence of self-drive cars, we talk to natural language processing assistants and gain data-driven based weather predictions. Amazon uses machine learning to improve the quality of the recommendations they make to their customers and many more everyday examples. Machine learning techniques have also been used for various purposes in software engineering and specifically software testing. While software bugs cost billions of dollars every year, it is a widely known concern that the maintenance of software requires a large amount of human effort and computing resources to analyze faulty code and devise a repair solution for the problem. It is therefore desirable to improve testing and debugging processes with smart automated techniques. The topics covered in this practical course include: application of machine learning in Software Defect Prediction, Test suite refinement, Fault localization and Debugging. Students will be given a software project (in Java or C++), respective test cases and needed tools. Students are expected to form teams of 2 students at the beginning of the semester. Teams will choose one of the main topics mentioned earlier and then, they will be given a list of suggested topics for the lab project. They are also encouraged to come up with their own topics. The course is divided into two parts. For the first part, teams will work on the data preparation and processing as weekly tasks. At the end of the first part, teams are expected to hand in a short presentation of how they want to proceed: the exact problem they want to solve and the machine learning algorithms they want to apply. During the second half of the course, teams will be required to develop a small project that implements and evaluates the previously-chosen topics.
Links TUMonline-Eintrag
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