Praktikum: Exploring the Relationship between Design Metrics and Software Diagnosability using Machine Learning (IN2106, IN0012, IN4238)
Lehrveranstaltung 0000004604 im SS 2018
Basisdaten
| LV-Art | Praktikum |
|---|---|
| Umfang | 6 SWS |
| betreuende Organisation | Informatik 4 - Lehrstuhl für Software & Systems Engineering (Prof. Pretschner) |
| Dozent(inn)en |
Mojdeh Golagha Leitung/Koordination: Alexander Pretschner Mitwirkende: Thomas Hutzelmann |
| Termine |
Do, 15:00–17:30, MI 01.09.014 sowie 1 einzelner oder verschobener Termin |
Zuordnung zu Modulen
-
IN2106: Master-Praktikum / Advanced Practical Course
Dieses Modul ist in den folgenden Katalogen enthalten:- weitere Module aus anderen Fachrichtungen
weitere Informationen
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| 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. Machine learning techniques have 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 localize faulty code and devise a repair solution for the problem. The objective of a diagnosability analysis is to estimate the expected effort and preciseness of the bug localization process. The associated measurement cannot be defined in abstracto, since faults are always revealed in a certain testing context. As we measure diagnosability at the design level, and since the measurement depends on the testing context, we need to qualify the testing context at the design level. The abstraction of the testing context is called a test strategy. The goal of this practical course is to empirically explore the relationship between existing object-oriented coupling, cohesion, and inheritance measures and the probability of fault localization during testing. In other words, we wish to better understand the relationship between existing design measurement in OO systems and the diagnosability of the software developed. |
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| Links |
E-Learning-Kurs (z. B. Moodle) TUMonline-Eintrag |