Testing scientific software: techniques for automatic detection of metamorphic relations

2015 Spring.Includes bibliographical references.Scientific software plays an important role in critical decision making in fields such as the nuclear industry, medicine, and the military. Systematic testing of such software can help to ensure that it works as expected. Comprehensive, automated software testing requires an oracle to check whether the output produced by a test case matches the expected behavior of the program. But the challenges in creating suitable oracles limit the ability to perform automated testing of scientific software. For some programs, creating an oracle may be not possible since the correct output is not known a priori. Further, it may be impractical to implement an oracle for an arbitrary input due to the complexity of a program. The software testing community refers to such programs as non-testable. Many scientific programs fall into this category of non-testable programs, since they are either written to find answers that are previously unknown or they perform complex calculations. In this work, we developed techniques to automatically predict metamorphic relations by analyzing the program structure. These metamorphic relations can serve as automated partial test oracles in scientific software. Metamorphic testing is a method for automating the testing process for programs without test oracles. This technique operates by checking whether a program behaves according to a certain set of properties called metamorphic relations. A metamorphic relation is a relationship between multiple input and output pairs of the program. It specifies how the output should change following a specific change made to the input. A change in the output that differs from what is specified by the metamorphic relation indicates a fault in the program. Metamorphic testing can be effective in testing machine learning applications, bioinformatics programs, health-care simulations, partial differential equations and other programs. Unfortunately, finding appropriate metamorphic relations for use in metamorphic testing remains a labor intensive task that is generally performed by a domain expert or a programmer. In this work we applied novel machine learning based approaches to automatically derive metamorphic relations. We first evaluated the effectiveness of modeling the metamorphic relation prediction problem as a binary classification problem. We found that support vector machines are the most effective binary classifiers for predicting metamorphic relations. We also found that using walk-based graph kernels for feature extraction from graph-based program representations further improves the prediction accuracy. In addition, incorporating mathematical properties of operations in the graph kernel computation improves the prediction accuracy. Further, we found that control flow information of a function are more effective than data dependency information for predicting metamorphic relations. Finally we investigated the possibility of creating multi-label classifiers that can predict multiple metamorphic relations using a single classifier. Our empirical studies show that multi-label classifiers are not effective as binary classifiers for predicting metamorphic relations. Automated testing will make the testing process faster, reduce the testing cost and make it more reliable. Automated testing requires automated test oracles. Automatically discovering metamorphic relations is an important step towards automating oracle creation. Work presented here is the first attempt towards developing automated techniques for deriving metamorphic relations. Our work contributes toward automating the testing process of programs that face oracle problems

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Real patient and its virtual twin : application of quantitative systems toxicology modelling in the cardiac safety assessment of citalopram

Abstract. A quantitative systems toxicology (QST) model for citalopram was established to simulate, in silico, a ‘virtual twin’ of a real patient to predict the occurrence of cardiotoxic events previously reported in patients under various clinical conditions. The QST model considers the effects of citalopram and its most notable electrophysiologically active primary (desmethylcitalopram) and secondary (didesmethylcitalopram) metabolites, on cardiac electrophysiology. The in vitro cardiac ion channel current inhibition data was coupled with the biophysically detailed model of human cardiac electrophysiology to investigate the impact of (i) the inhibition of multiple ion currents (IKr, IKs, ICaL); (ii) the inclusion of metabolites in the QST model; and (iii) unbound or total plasma as the operating drug concentration, in predicting clinically observed QT prolongation. The inclusion of multiple ion channel current inhibition and metabolites in the simulation with unbound plasma citalopram concentration provided the lowest prediction error. The predictive performance of the model was verified with three additional therapeutic and supra-therapeutic drug exposure clinical cases. The results indicate that considering only the hERG ion channel inhibition of only the parent drug is potentially misleading, and the inclusion of active metabolite data and the influence of other ion channel currents should be considered to improve the prediction of potential cardiac toxicity. Mechanistic modelling can help bridge the gaps existing in the quantitative translation from preclinical cardiac safety assessment to clinical toxicology. Moreover, this study shows that the QST models, in combination with appropriate drug and systems parameters, can pave the way towards personalised safety assessment

Research and Technology Objectives and Plans Summary (RTOPS)

This publication represents the NASA research and technology program for FY 1985. It is a compilation of the Summary portions of each of the RTOPs (Research and Technology Objectives and Plans) used for management review and control of research currently in progress throughout NASA. The RTOP summary is designed to facilitate communication and coordination among concerned technical personnel in government, in industry, and in universities. The first section containing citations and abstracts of the RTOPs is followed by four indexes: Subject, Technical Monitor, Responsible NASA Organization, and RTOP number

Digital Pathology: The Time Is Now to Bridge the Gap between Medicine and Technological Singularity

Digitalization of the imaging in radiology is a reality in several healthcare institutions worldwide. The challenges of filing, confidentiality, and manipulation have been brilliantly solved in radiology. However, digitalization of hematoxylin- and eosin-stained routine histological slides has shown slow movement. Although the application for external quality assurance is a reality for a pathologist with most of the continuing medical education programs utilizing virtual microscopy, the abandonment of traditional glass slides for routine diagnostics is far from the perspectives of many departments of laboratory medicine and pathology. Digital pathology images are captured as images by scanning and whole slide imaging/virtual microscopy can be obtained by microscopy (robotic) on an entire histological (microscopic) glass slide. Since 1986, services using telepathology for the transfer of images of anatomic pathology between detached locations have benefited countless patients globally, including the University of Alberta. The purpose of specialist recertification or re-validation for the Royal College of Pathologists of Canada belonging to the Royal College of Physicians and Surgeons of Canada and College of American Pathologists is a milestone in virtual reality. Challenges, such as high bandwidth requirement, electronic platforms, the stability of the operating systems, have been targeted and are improving enormously. The encryption of digital images may be a requirement for the accreditation of laboratory services—quantum computing results in quantum-mechanical phenomena, such as superposition and entanglement. Different from binary digital electronic computers based on transistors where data are encoded into binary digits (bits) with two different states (0 and 1), quantum computing uses quantum bits (qubits), which can be in superpositions of states. The use of quantum computing protocols on encrypted data is crucial for the permanent implementation of virtual pathology in hospitals and universities. Quantum computing may well represent the technological singularity to create new classifications and taxonomic rules in medicine