At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model’s behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model’s behavior, precision to how accurate humans are in those predictions, and e...

If there are many independent, identically distributed observations governed by a smooth, finite-dimensional statistical model, the Bayes estimate and the maximum likelihood estimate will be close. Furthermore, the posterior distribution of the parameter vector around the posterior mean will be close to the distribution of the maximum likelihood estimate around truth. Thus, Bayesian confidence ...

Determining and understanding contributions to a shared code base can be helpful for monitoring developer efficiency, allocating work items and understanding how and why changes were made. To support this task, we introduce a method for deriving contribution using test execution results and code coverage reports. Our visualization tool, Teamline, then presents these metrics in two different vie...

Although there are quite a few approaches to Coverage Directed test Generation aided by Machine Learning which have been applied successfully to small and medium size digital designs, it is not clear how they would scale on more elaborate industrial-level designs. This paper evaluates one of these techniques, called MicroGP, on a fully fledged industrial design. The results indicate relative su...

Forecaster perceptions of major convective outbreaks include the notion that these events occur within relatively large regions of meteorological conditions favorable for the development of significant severe weather, particularly tornadoes. With recent studies developing a rigorous and scientifically repeatable method of identifying these events and distinguishing them from intermediate or mar...

We propose a dependence-based representation for object-oriented programs, named Call-based Object-Oriented System Dependence Graph (COSDG). Apart from structural features, COSDG captures important object-oriented features such as class, inheritance, and polymorphism. Novel features of COSDG include details of method visibility in a derived class, and different types of method call edges to dis...

Software Model Checkers have shown outstanding performance improvements in recent times. Moreover, for specific use cases, formal verification techniques have shown to be highly effective, leading to a number of high-profile success stories. However, widespread adoption remains unlikely in the short term and one of the remaining obstacles in that direction is the vast number of instances which ...

Prioritization of test cases is generally done to reduce the cost of regression testing. We prioritize our test cases so that those which are more important, by some measure, are made to run earlier in the testing phase. There exists a large variety of prioritization techniques in the literature, we have basically used coverage-based prioritization techniques (i.e., prioritization in terms of t...

Testers use coverage data for test suite quality assessment, stopping criteria definition, and effort allocation. However, as the complexity of products and testing processes increases, the cost of coverage data collection may grow significantly, jeopardizing its potential application. To mitigate this problem this paper presents the concept of “disposable coverage instrumentation” – coverage i...