Bugs in compilers, which are critical infrastructure today, can have outsized negative impacts. Mutational fuzzers aid compiler bug detection by systematically mutating compiler inputs, i.e., programs. Their effectiveness depends on the quality of the mutators used. Yet, no prior work used compiler bug histories as a source of mutators.

While Python is designed as a cross-platform language, real-world applications encounter portability failures when deployed across different operating systems. We present the first large-scale empirical study of cross-OS portability issues in Python, analyzing 2,042 open-source repositories using two complementary approaches: systematic cross-OS test re-execution and manual analysis of GitHub issues. Our cross-platform testing of 500 projects reveals that 11.2% exhibit OS-dependent test failures. Through systematic analysis of 240 GitHub issues, we confirm 102 genuine portability problems spanning 95 additional projects. We develop a comprehensive taxonomy identifying 7 primary failure categories—with file/directory operations, process management, and library dependencies being most prevalent—along with 24 distinct sub-categories, 15 diagnostic signatures, and 4 systematic repair patterns. Our evaluation reveals that existing static analysis tools provide minimal support for portability detection, while large language models achieve 40–79% accuracy in identifying issues and 50–77% success in generating fixes when provided with structured guidance. Through 33 contributed pull requests, we demonstrate practical applicability and developer acceptance (17 merged, zero rejected) of our findings.

Handwritten mathematical expression recognition (HMER) is a challenging task due to factors such as ambiguity, variety of writing styles, and complexity of two-dimensional writing. In this paper, we identify challenges in HMER applications through experiments that simulate real scenarios that go far beyond the usual cases found in literature: variations on luminance; different stroke width, inclination and color; different background pattern; and partially shaded images. The results of state-of-the-art methods (as TAP and Dense-WAP) and a commercial tool (MathPix) are analyzed, using the CROHME 2016 database. We proved that, although the area has had a lot of improvement in recent years, there are still issues to overcome.

Flaky tests are tests whose outcomes are non-deterministic. Despite the recent research activity on this topic, no effort has been made on understanding the vocabulary of flaky tests. This work proposes to automatically classify tests as flaky or not based on their vocabulary. Static classification of flaky tests is important, for example, to detect the introduction of flaky tests and to search for flaky tests after they are introduced in regression test suites.