Program fixes must preserve passing tests while fixing failing ones. Validating these properties requires test oracles that distinguish passing from failing runs.We introduce BASHIRI, a tool that learns failure oracles from test suites with labeled outcomes using execution features. BASHIRI leverages execution-feature-driven debugging to collect program execution features and trains interpretable models as testing oracles. Our evaluation shows that BASHIRI predicts test outcomes with 95% accuracy, effectively identifying failing runs. BASHIRI is available as an open-source tool at https://github.com/smythi93/bashiriA demonstration video is available at https://youtu.be/D2mJkCtSXtM

In recent years, automatic program repair has gained much attention in the research community. Generally, program repair approaches consider a faulty program and a test suite that captures the program's intended behavior. The goal is automatically generating a patch that corrects the fault by identifying the faulty code locations, suggesting a candidate fix, and validating it against the provided tests. However, most existing program repair tools focus on Java or C programs, while Python, one of the most popular programming languages, lacks approaches that work on it.

Why does my program fail? We present a novel and general technique to automatically determine failure causes and conditions, using logical properties over input elements: “The program fails if and only if int( ) > len( ) holds—that is, the given is larger than the length.” Our AVICENNA prototype uses modern techniques for inferring properties of passing and failing inputs and validating and refining hypotheses by having a constraint solver generate supporting test cases to obtain such diagnoses. As a result, AVICENNA produces crisp and expressive diagnoses even for complex failure conditions, considerably improving over the state of the art with diagnoses close to those of human experts.

Statistical fault localization aims at detecting execution features that correlate with failures, such as whether individual lines are part of the execution. We introduce SFLKit, an out-of-the-box workbench for statistical fault localization. The framework provides straightforward access to the fundamental concepts of statistical fault localization. It supports five predicate types, four coverage-inspired spectra, like lines, and 44 similarity coefficients, e.g., TARANTULA or OCHIAI, for statistical program analysis.