Upulee Kanewala

TL;DR: In this article, the authors identified specific challenges, proposed solutions, and unsolved problems faced when testing scientific software and identified methods to potentially overcome these challenges and their limitations, and described unsolved challenges and how software engineering researchers and practitioners can help to overcome them.

TL;DR: A machine learning approach for predicting metamorphic relations that uses a graph‐based representation of a programme to represent control flow and data dependency information and shows that a graph kernel that evaluates the contribution of all paths in the graph has the best accuracy and that control flow information is more useful than data dependency Information.

TL;DR: This work presents a novel approach for automatically predicting metamorphic relations using machine learning techniques and shows the effectiveness of the method using a set of real world functions often used in scientific applications.

TL;DR: This paper examines three techniques that are used to test programs without oracles: metamorphic testing, Run-time Assertions and developing test oracles using machine learning, and there is potential to increase the level of automation of these techniques, thereby reducing the required level of domain knowledge.

TL;DR: In this article, a classification of test oracles based on a taxonomy that considers their source of information and notations is presented, and the maturity of this field using coauthorship networks among studies published between 1978 and 2013.