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.

More machine learning (ML) models are introduced to the field of Software Engineering (SE) and reached a stage of maturity to be considered for real-world use; But the real world is complex, and testing these models lacks often in explainability, feasibility and computational capacities. Existing research introduced meta-morphic testing to gain additional insights and certainty about the model, by applying semantic-preserving changes to input-data while observing model-output. As this is currently done at random places, it can lead to potentially unrealistic datapoints and high computational costs. With this work, we introduce genetic search as an aid for metamorphic testing in SE ML. Exploiting the delta in output as a fitness function, the evolutionary intelligence optimizes the transformations to produce higher deltas with less changes. We perform a case study minimizing F1 and MRR for Code2Vec on a representative sample from java-small with both genetic and random search. Our results show that within the same amount of time, genetic search was able to achieve a decrease of 10% in F1 while random search produced 3% drop.

We present HasBugs, an extensible and manually-curated dataset of real-world 25 Haskell Bugs from 6 open source repositories. We provide a faulty, tested, and fixed version of each bug in our dataset with reproduction packages, description, and bug context. For technical users, the dataset is meant to either help researchers adapt techniques from other programming languages to Haskell or to provide a human-verified gold standard for tools evaluation and enable future reproducibility. We also see applicability for qualitative research, e.g., by analysis of bug lifecycles and comparison to other languages. We provide a companion website for easy access and overview under https://ciselab.github.io/HasBugs/.

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.

Metamorphic testing is a well-established testing technique that has been successfully applied in various domains, including testing deep learning models to assess their robustness against data noise or malicious input. Currently, metamorphic testing approaches for machine learning (ML) models focused on image processing and object recognition tasks. Hence, these approaches cannot be applied to ML targeting program analysis tasks. In this paper, we extend metamorphic testing approaches for ML models targeting software programs. We present LAMPION, a novel testing framework that applies (semantics preserving) metamorphic transformations on the test datasets. LAMPION produces new code snippets equivalent to the original test set but different in their identifiers or syntactic structure. We evaluate LAMPION against CodeBERT, a state-of-the-art ML model for Code-To-Text tasks that creates Javadoc summaries for given Java methods. Our results show that simple transformations significantly impact the target model behavior, providing additional information on the models reasoning apart from the classic performance metric.