Spectrum-based fault localization and its formulas often struggle with large spectra containing many expressions irrelevant to the fault, which impacts its overall effectiveness. Spectra can inflate for large programs or on finer granularity, such as expression-level coverage from other languages like Haskell. To address this, we introduce 25 rules to filter the spectra based on type information, AST attributes, and test results. These aim to reduce the suspiciousness of innocent locations (bug-free expressions) and improve the performance of SBFL formulas w.r.t. TOP50 and TOP100 metrics. Our experiment, conducted on 11 Haskell programs, shows that individual filters significantly reduce spectra size, although some data points (faulty expressions) become unsolvable. By applying established SBFL formulas like Ochiai and Tarantula to these reduced spectra, we observe average improvements of up to 40% w.r.t. TOP50 for individual soft rules, such as proximity to failure. Combining the best-performing filters yields improvements of 45.5% for Ochiai, 67.4% for DStar2, and 45.5% for Tarantula. The most effective filtering rules over all formulas captured proximity to failing expressions, usage of a non-unique type, and whether a failing test covered the expression. Our results suggest that simple, straightforward filters can produce substantial performance gains. We further identify 4 uncovered bugs originating from code generation (common in functional programming) and system tests, which can not be addressed purely by spectrum-based fault localization.

We present Spectacular, a new tool for automatically discovering candidate laws for use in property-based testing. By using the recently-developed technique of ECTAs (Equality-Constrained Tree Automata), Spectacular improves upon previous approaches such as QuickSpec: it can explore vastly larger program spaces and start generating candidate laws within 20 seconds from a benchmark where QuickSpec runs for 45 minutes and then crashes (due to memory limits, even on a 256 GB machine). Thanks to the ability of ECTAs to efficiently search constrained program spaces, Spectacular is fast enough to find candidate laws in more generally typed settings than the monomorphized one, even for signatures with dozens of functions.

Automatic program repair (APR) regularly faces the challenge of overfitting patches --- patches that pass the test suite, but do not actually address the problems when evaluated manually. Currently, overfit detection requires manual inspection or an oracle making quality control of APR an expensive task. With this work, we want to introduce properties in addition to unit tests for APR to address the problem of overfitting. To that end, we design and implement PropR, a program repair tool for Haskell that leverages both property-based testing (via QuickCheck) and the rich type system and synthesis offered by the Haskell compiler. We compare the repair-ratio, time-to-first-patch and overfitting-ratio when using unit tests, property-based tests, and their combination. Our results show that properties lead to quicker results and have a lower overfit ratio than unit tests. The created overfit patches provide valuable insight into the underlying problems of the program to repair (e.g., in terms of fault localization or test quality). We consider this step towards fitter, or at least insightful, patches a critical contribution to bring APR into developer workflows.