AUTOMATIC TEST GENERATION BASED ON CONSTRAINTS

It seems to be a very hard task to enhance the properties of widespreadly used automatic test pattern generation algorithms. Experiences show that achievements are sometimes not worth the effort. In the authors' opinion this fact stems from the basically 'algorithm oriented' nature of research made in the past. A new experimental framework is presented for the problem, considering network representation and search control algorithms as equally important parts. The network is represented by object- oriented data-flow networks, the search control algorithm is based on constraint satisfaction, and a special kind of dependency directed backtracking which we call constraint slackening. Similar methods were proved to be very useful in automatic system diagnosis by DAVIS (1985) and others, although have not been introduced to testing yet. This paper summarises the basic notions of constraint satisfaction, the potential advantages of using it for building test generation systems, and shows implementational details of a test generation system, based on constraints. Experiences of the run-time tests show that constraint-based test generation can be highly efficient

Semantic Based Automatic Question Generation using Artificial Immune System

This research introduces a semantic based Automatic Question Generation System using both Semantic Role Labeling and Named Entity Recognition techniques to convert the input sentence into a semantic pattern. A training phase applied to build a classifier using an Artificial Immune System that will be able to classify the patterns according to the question type. The question types considered here are set of WH-questions like who, when, where, why, and how. Then a pattern matching phase is applied to select the best matching question pattern for the test sentence. The proposed system is tested against a set of sentences obtained from different sources like Wikipedia articles, TREC 2007dataset for question answering, and English book of grade II prep. The proposed model shows promising results in determining the question type with classification accuracy increases 95%, and also in generating (matching) the new question patterns with 87%

EFFICIENCY TEST OF AUTOMATIC TEST PATTERN GENERATION METHODS

Automatic Test Pattern Generation (ATPG) is unavoidable for large combinational circuits, However, since ATPG is a known NP-complet problem, this is a very CPU-time consuming process, Therefore choosing the optimal ATPG algorithm for an industrial test generation system can be an important question, However, this question cannot be easily answered because of the implementational and evaluation differences of the published algorithms, This paper presents a software frame, where any ATPG method and their heuristic can be easily implemented allowing a correct comparison between different methods, On the other hand the known ATPG methods cannot be ordered by quality, because their efficiency depends on the properties of the examined circuit. Therefore it seems to be reasonable to develop a hibrid strategy whose effectivity is independent of the circuit properties and near to the known strategies, The presented frame is an ideal environment for developing such a new method, Experimental results are also presented on some implemented algorithms and heuristics using a variety of MSI components and ISCAS'85 benchmark circuits

Controlling selective stimulations below a spinal cord hemisection using brain recordings with a neural interface system approach.

In this work we address the use of realtime cortical recordings for the generation of coherent, reliable and robust motor activity in spinal-lesioned animals through selective intraspinal microstimulation (ISMS). The spinal cord of adult rats was hemisectioned and groups of multielectrodes were implanted in both the central nervous system (CNS) and the spinal cord below the lesion level to establish a neural system interface (NSI). To test the reliability of this new NSI connection, highly repeatable neural responses recorded from the CNS were used as a pattern generator of an open-loop control strategy for selective ISMS of the spinal motoneurons. Our experimental procedure avoided the spontaneous non-controlled and non-repeatable neural activity that could have generated spurious ISMS and the consequent undesired muscle contractions. Combinations of complex CNS patterns generated precisely coordinated, reliable and robust motor actions

Test set generation and optimisation using evolutionary algorithms and cubical calculus.

As the complexity of modern day integrated circuits rises, many of the challenges associated with digital testing rise exponentially. VLSI technology continues to advance at a rapid pace, in accordance with Moore's Law, posing evermore complex, NP-complete problems for the test community. The testing of ICs currently accounts for approximately a third of the overall design costs and according to the Semiconductor Industry Association, the per-transistor test cost will soon exceed the per-transistor production cost. Given the need to test ICs of ever-increasing complexity and to contain the cost of test, the problems of test pattern generation, testability analysis and test set minimisation continue to provide formidable challenges for the research community. This thesis presents original work in these three areas. Firstly, a new method is presented for generating test patterns for multiple output combinational circuits based on the Boolean difference method and cubical calculus. The Boolean difference method has been largely overlooked in automatic test pattern generation algorithms due to its cumbersome, algebraic nature. It is shown that cubical calculus provides an elegant and economical technique for solving Boolean difference equations. Formal mathematical techniques are presented involving the Boolean difference and cubical calculus providing, a test pattern generation method that dispenses with the need for costly circuit simulations. The methods provide the basis for test generation algorithms which are suitable for computer implementation. Secondly, some of the core test pattern generation computations outlined above also provide the basis of a new method for computing testability measures such as controllability and observability. This method is effectively a very economical spin-off of the test pattern generation process using Boolean differences and cubical calculus.The third and largest part of this thesis introduces a new test set minimization algorithm, GA-MITS, based on an evolutionary optimization algorithm. This novel approach applies a genetic algorithm to find minimal or near minimal test sets while maintaining a given fault coverage. The algorithm is designed as a postprocessor to minimise test sets that have been previously generated by an ATPG system and is thus considered a static approach to the test set minimisation problem. It is shown empirically that GA-MITS is remarkably successful in minimizing test sets generated for the ISCAS-85 benchmark circuits and hence potentially capable of reducing the production costs of realistic digital circuits

Performance-Driven Metamorphic Testing of Cyber-Physical Systems

Cyber-physical systems (CPSs) are a new generation of systems, which integrate software with physical processes. The increasing complexity of these systems, combined with the un certainty in their interactions with the physical world, makes the definition of effective test oracles especially challenging, facing the well-known test oracle problem. Metamorphic testing has shown great potential to alleviate the test oracle problem by exploiting the relations among the inputs and outputs of different executions of the system, so-called metamorphic relations (MRs). In this article, we propose an MR pattern called PV for the identification of performance-driven MRs, and we show its applicability in two CPSs from different domains, which are automated navigation systems and elevator control systems. For the evaluation, we as sessed the effectiveness of this approach for detecting failures in an open-source simulation-based autonomous navigation system, as well as in an industrial case study from the elevation domain. We derive concrete MRs based on the PV pattern for both case studies, and we evaluate their effectiveness with seeded faults. Results show that the approach is effective at detecting over 88% of the seeded faults, while keeping the ratio of FPs at 4% or lower.European Union's Horizon 2020 Research and Innovation Programme (Grant Number: 871319)Junta de Andalucía US-1264651 (APOLO)Junta de Andalucía P18-FR-2895 (EKIPMENT-PLUS)Ministerio de Ciencia e Innovación RTI2018-101204-B-C21 (HORATIO)Mondragon Unibertsitatea IT1519-2

Entropy Generation/Availability Energy Loss Analysis Inside MIT Gas Spring and "Two Space" Test Rigs

The results of the entropy generation and availability energy loss analysis under conditions of oscillating pressure and oscillating helium gas flow in two Massachusetts Institute of Technology (MIT) test rigs piston-cylinder and piston-cylinder-heat exchanger are presented. Two solution domains, the gas spring (single-space) in the piston-cylinder test rig and the gas spring + heat exchanger (two-space) in the piston-cylinder-heat exchanger test rig are of interest. Sage and CFD-ACE+ commercial numerical codes are used to obtain 1-D and 2-D computer models, respectively, of each of the two solution domains and to simulate the oscillating gas flow and heat transfer effects in these domains. Second law analysis is used to characterize the entropy generation and availability energy losses inside the two solution domains. Internal and external entropy generation and availability energy loss results predicted by Sage and CFD-ACE+ are compared. Thermodynamic loss analysis of simple systems such as the MIT test rigs are often useful to understand some important features of complex pattern forming processes in more complex systems like the Stirling engine. This study is aimed at improving numerical codes for the prediction of thermodynamic losses via the development of a loss post-processor. The incorporation of loss post-processors in Stirling engine numerical codes will facilitate Stirling engine performance optimization. Loss analysis using entropy-generation rates due to heat and fluid flow is a relatively new technique for assessing component performance. It offers a deep insight into the flow phenomena, allows a more exact calculation of losses than is possible with traditional means involving the application of loss correlations and provides an effective tool for improving component and overall system performance