Refinement type contracts for verification of scientific investigative software

Our scientific knowledge is increasingly built on software output. User code which defines data analysis pipelines and computational models is essential for research in the natural and social sciences, but little is known about how to ensure its correctness. The structure of this code and the development process used to build it limit the utility of traditional testing methodology. Formal methods for software verification have seen great success in ensuring code correctness but generally require more specialized training, development time, and funding than is available in the natural and social sciences. Here, we present a Python library which uses lightweight formal methods to provide correctness guarantees without the need for specialized knowledge or substantial time investment. Our package provides runtime verification of function entry and exit condition contracts using refinement types. It allows checking hyperproperties within contracts and offers automated test case generation to supplement online checking. We co-developed our tool with a medium-sized ($\approx$3000 LOC) software package which simulates decision-making in cognitive neuroscience. In addition to helping us locate trivial bugs earlier on in the development cycle, our tool was able to locate four bugs which may have been difficult to find using traditional testing methods. It was also able to find bugs in user code which did not contain contracts or refinement type annotations. This demonstrates how formal methods can be used to verify the correctness of scientific software which is difficult to test with mainstream approaches

A partial oracle for uniformity statistics

This paper investigates the problem of testing implementations of uniformity statistics. In this paper we used Metamorphic Testing to address the oracle problem, of checking the output of one or more test executions, for uniformity statistics. We defined a partial oracle that uses regression analysis (a Regression Model based Metamorphic Relation). We investigated the effectiveness of our partial oracle. We found that the technique can achieve mutation scores ranging from 77.78% to 100%, and tends towards higher mutation scores in this range. These results are promising, and suggest that the Regression Model based Metamorphic Relation approach is a viable method of alleviating the oracle problem in implementations of uniformity statistics, and potentially other classes of statistics e.g. correlation statistics

Power-Aware high-level synthesis flow for mapping fpga designs

Present Field Programmable Gate Array (FPGA) manufacturers incorporate multi-millions of logic resources which enables hardware designers to design applications extending to large scales. However, handling such applications by existing FPGA Computer Aided Design (CAD) flow requires more improvement in terms of area, performance and power efficiency considerations. The current CAD flow requires the input design to be in Register Transfer Level (RTL). RTL input designs limit the design productivity only to hardware experts in performing analysis for various optimisations. Optimising RTL designs manually are increasingly hard. High- Level Synthesis (HLS) is an approach capable of increasing the design productivity of hardware applications compared to commonly used Hardware Description Languages (HDLs) and is known to be an intelligent approach for performing optimisations at a higher level of abstraction. In this paper, an approach that follows the HLS flow to cater to the mapping of FPGA applications in a power efficient manner using a communicationaware partitioning strategy is proposed. From experiments, it was possible to achieve an average reduction of 8.39% routing thermal power and 3.34% total power using the proposed approac