Machine Learning (ML) based solutions are becoming increasingly popular and pervasive. When testing such solutions, there is a tendency to focus on improving the ML metrics such as the F1-score and accuracy at the expense of ensuring business value and correctness by covering business requirements. In this work, we adapt test planning methods of classical software to ML solutions. We use combinatorial modeling methodology to define the space of business requirements and map it to the ML solution data, and use the notion of data slices to identify the weaker areas of the ML solution and strengthen them. We apply our approach to three real-world case studies and demonstrate its value.

A common industrial challenge is that of analyzing large legacy free text test suites in order to comprehend their functional content. The analysis results are used for different purposes, such as dividing the test suite into disjoint functional parts for automation and management purposes, identifying redundant test cases, and extracting models for combinatorial test generation while reusing the legacy test suite. Currently the analysis is performed manually, which hinders the ability to analyze many such large test suites due to time and resource constraints.

The growing gap between the advanced capabilities of static compilers as reflected in benchmarking results and the actual performance that users experience in real-life scenarios makes client-side dynamic optimization technologies imperative to the domain of static languages. Dynamic optimization of software distributed in the form of a platform-agnostic Intermediate-Representation (IR) has been very successful in the domain of managed languages, greatly improving upon interpreted code, especially when online profiling is used. However, can such feedback-directed IR-based dynamic code generation be viable in the domain of statically compiled, rather than interpreted, languages? We show that fat binaries, which combine the IR together with the statically compiled executable, can provide a practical solution for software vendors, allowing their software to be dynamically optimized without the limitation of binary-level approaches, which lack the high-level IR of the program, and without the warm-up costs associated with the IR-only software distribution approach. We describe and evaluate the fat-binary-based runtime compilation approach using SPECint2006, demonstrating that the overheads it incurs are low enough to be successfully surmounted by dynamic optimization. Building on Java JIT technologies, our results already improve upon common real-world usage scenarios, including very small workloads.