Search for dissertations about: "machine learning software testing"

Showing result 1 - 5 of 14 swedish dissertations containing the words machine learning software testing.

  2. 1. Learning-based Software Testing using Symbolic Constraint Solving Methods

    Author : Fei Niu; Karl Meinke; Reiner Hähnle; KTH; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Testing; Machine learning; Symbolic constraint solving; Model checking;

    Abstract : Software testing remains one of the most important but expensive approaches to ensure high-quality software today. In order to reduce the cost of testing, over the last several decades, various techniques such as formal verification and inductive learning have been used for test automation in previous research. READ MORE

  4. 2. Learning-based Testing for Automotive Embedded Systems : A requirements modeling and Fault injection study

    Author : Hojat Khosrowjerdi; Karl Meinke; Dilian Gurov; Cristina Seceleanu; KTH; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Machine learning; fault injection; requirements testing; embedded systems; model checking; automotive software; requirements modeling; Computer Science; Datalogi;

    Abstract : This thesis concerns applications of learning-based testing (LBT) in the automotive domain. In this domain, LBT is an attractive testing solution, since it offers a highly automated technology to conduct safety critical requirements testing based on machine learning. READ MORE

  5. 3. Understanding and Managing Non-functional Requirements for Machine Learning Systems

    Author : Khan Mohammad Habibullah; Göteborgs universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Requirements Engineering; Quality Requirements; Machine Learning; NFRs; Non-functional Requirements; Non-functional Requirements; NFRs; Machine Learning; Quality Requirements; Requirements Engineering;

    Abstract : Background: Machine Learning (ML) systems learn using big data and solve a wide range of prediction and decision making problems that would be difficult to solve with traditional systems. However, increasing use of ML in complex and safety-critical systems has raised concerns about quality requirements, which are defined as Non-Functional requirements (NFRs). READ MORE

  6. 4. Journeys in vector space: Using deep neural network representations to aid automotive software engineering

    Author : Dhasarathy Parthasarathy; Chalmers tekniska högskola; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; automotive software design and testing; generative adversarial networks; latent space arithmetic; generative AI; explainable AI; large language models;

    Abstract : Context - The automotive industry is in the midst of a transformation where software is becoming the primary tool for delivering value to customers. While this has vastly improved their product offerings, vehicle manufacturers are facing an urgent need to continuously develop, test, and deliver functionality, while maintaining high levels of quality. READ MORE

  7. 5. Machine Learning-Assisted Performance Assurance

    Author : Mahshid Helali Moghadam; Markus Bohlin; Mehrdad Saadatmand; Markus Borg; Björn Lisper; Shaukat Ali; Mälardalens högskola; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Computer Science; datavetenskap;

    Abstract : With the growing involvement of software systems in our life, assurance of performance, as an important quality characteristic, rises to prominence for the success of software products. Performance testing, preservation, and improvement all contribute to the realization of performance assurance. READ MORE