arXiv subjects

M M Abid Naziri

Publications and source records attributed to M M Abid Naziri.

3 recordsLinked to original sources

arXiv2026

Improving Deep Learning Library Testing with Machine Learning

Deep Learning (DL) libraries like TensorFlow and Pytorch simplify machine learning (ML) model development but are prone to bugs due to their complex design. Bug-finding techniques exist, but without precise API specifications, they produce many false alarms. Existing methods to mine API specifications lack accuracy. We explore using ML classifiers to determine input validity. We hypothesize that tensor shapes are a precise abstraction to encode concrete inputs and capture relationships of the data. Shape abstraction severely reduces problem dimensionality, which is important to facilitate ML training. Labeled data are obtained by observing runtime outcomes on a sample of inputs and classifiers are trained on sets of labeled inputs to capture API constraints. Our evaluation, conducted over 183 APIs from TensorFlow and Pytorch, shows that the classifiers generalize well on unseen data with over 91% accuracy. Integrating these classifiers into the pipeline of ACETest, a SoTA bug-finding technique, improves its pass rate from ~29% to ~61%. Our findings suggest that ML-enhanced input classification is an important aid to scale DL library testing.

cs.SE↗

arXiv2026

Testing Deep Learning Libraries via Neurosymbolic Constraint Learning

Deep Learning (DL) libraries (e.g., PyTorch) are popular in AI development. These libraries are complex and contain bugs. Researchers have proposed various bug-finding techniques for such libraries. Yet, there is much room for improvement. A key challenge in testing DL libraries is the lack of API specifications. Prior testing approaches often inaccurately model the input specifications of DL APIs, resulting in missed valid inputs that could reveal bugs or false alarms due to invalid inputs. To address this challenge, we develop Centaur -- the first neurosymbolic technique to test DL library APIs using dynamically learned input constraints. Centaur leverages the key idea that formal API constraints can be learned from a small number of automatically generated seed inputs, and that the learned constraints can be solved using SMT solvers to generate valid and diverse test inputs. We develop a novel grammar that represents first-order logic formulae over API parameters and expresses tensor-related properties (e.g., shape, data types) as well as relational properties between parameters. We use the grammar to guide a Large Language Model (LLM) to enumerate syntactically correct candidate rules, validated using seed inputs. Further, we develop a custom refinement strategy to prune the set of learned rules to eliminate spurious or redundant rules. We use the learned constraints to systematically generate valid and diverse inputs by integrating SMT-based solving with randomized sampling. We evaluate Centaur for testing PyTorch and TensorFlow. Our results show that Centaur's constraints have a recall of 94.0% and a precision of 94.0% on average. In terms of coverage, Centaur covers 203, 150, and 9,608 more branches than TitanFuzz, ACETest and Pathfinder, respectively. Using Centaur, we also detect 26 new bugs in PyTorch and TensorFlow, 18 of which are confirmed.

cs.SE↗

arXiv2025

Misbehavior Forecasting for Focused Autonomous Driving Systems Testing

Simulation-based testing is the standard practice for assessing the reliability of self-driving cars' software before deployment. Existing bug-finding techniques are either unreliable or expensive. We build on the insight that near misses observed during simulations may point to potential failures. We propose Foresee, a technique that identifies near misses using a misbehavior forecaster that computes possible future states of the ego-vehicle under test. Foresee performs local fuzzing in the neighborhood of each candidate near miss to surface previously unknown failures. In our empirical study, we evaluate the effectiveness of different configurations of Foresee using several scenarios provided in the CARLA simulator on both end-to-end and modular self-driving systems and examine its complementarity with the state-of-the-art fuzzer DriveFuzz. Our results show that Foresee is both more effective and more efficient than the baselines. Foresee exposes 128.70% and 38.09% more failures than a random approach and a state-of-the-art failure predictor while being 2.49x and 1.42x faster, respectively. Moreover, when used in combination with DriveFuzz, Foresee enhances failure detection by up to 93.94%.

cs.SE↗