arXiv subjects

Yeshayahu Weiss

Publications and source records attributed to Yeshayahu Weiss.

4 recordsLinked to original sources

arXiv2025

Black-Box Bug-Amplification for Multithreaded Software

Bugs, especially those in concurrent systems, are often hard to reproduce because they manifest only under rare conditions. Testers frequently encounter failures that occur only under specific inputs, even when occurring with low probability. We propose an approach to systematically amplify the occurrence of such elusive bugs. We treat the system under test as a black-box and use repeated trial executions to train a predictive model that estimates the probability of a given input configuration triggering a bug. We evaluate this approach on a dataset of 17 representative concurrency bugs spanning diverse categories. Several model-based search techniques are compared against a brute-force random sampling baseline. Our results show that an ensemble of regression models can significantly increase bug occurrence rates across nearly all scenarios, often achieving an order-of-magnitude improvement over random sampling. The contributions of this work include: (i) a novel formulation of bug-amplification as a rare-event regression problem; (ii) an empirical evaluation of multiple techniques for amplifying bug occurrence, demonstrating the effectiveness of model-guided search; and (iii) a practical, non-invasive testing framework that helps practitioners expose hidden concurrency faults without altering the internal system architecture.

cs.SE↗

arXiv2023

Provengo: A Tool Suite for Scenario Driven Model-Based Testing

We present Provengo, a comprehensive suite of tools designed to facilitate the implementation of Scenario-Driven Model-Based Testing (SDMBT), an innovative approach that utilizes scenarios to construct a model encompassing the user's perspective and the system's business value while also defining the desired outcomes. With the assistance of Provengo, testers gain the ability to effortlessly create natural user stories and seamlessly integrate them into a model capable of generating effective tests. The demonstration illustrates how SDMBT effectively addresses the bootstrapping challenge commonly encountered in model-based testing (MBT) by enabling incremental development, starting from simple models and gradually augmenting them with additional stories.

cs.SE↗

arXiv2022

Generalized Coverage Criteria for Combinatorial Sequence Testing

We present a new model-based approach for testing systems that use sequences of actions and assertions as test vectors. Our solution includes a method for quantifying testing quality, a tool for generating high-quality test suites based on the coverage criteria we propose, and a framework for assessing risks. For testing quality, we propose a method that specifies generalized coverage criteria over sequences of actions, which extends previous approaches. Our publicly available tool demonstrates how to extract effective test suites from test plans based on these criteria. We also present a Bayesian approach for measuring the probabilities of bugs or risks, and show how this quantification can help achieve an informed balance between exploitation and exploration in testing. Finally, we provide an empirical evaluation demonstrating the effectiveness of our tool in finding bugs, assessing risks, and achieving coverage.

cs.SE↗

arXiv2021

Testing Reactive Systems Using Behavioural Programming, a Model Centric Approach

Testing is a significant aspect of software development. As systems become complex and their use becomes critical to the security and the function of society, the need for testing methodologies that ensure reliability and detect faults as early as possible becomes critical. The most promising approach is the model-based approach where a model is developed that defines how the system is expected to behave and how it is meant to react. The tests are derived from the model and an analysis of the test results is conducted based on it. We will investigate the prospects of using the Behavioral Programming (BP) for a model-based testing (MBT) approach that we will develop. We will develop a natural language for representing the requirements. The model will be fed to algorithms that we will develop. This includes algorithms for the automatic creation of minimal sets of test cases that cover all of the system's requirements, analysing the results of the tests, and other tools that support the testing process. The focus of our methodology will be to find faults caused by the interaction between different requirements in ways that are difficult for the testers to detect. Specifically, we will focus our attention to concurrency issues such as deadlocks and logical race condition. We will use a variety of methods that are made possible by BP, such as non-deterministic execution of scenarios and use of in-code model-checking for building test scenarios and for finding minimal coverage of the test scenarios for the system requirements using Combinatorial Test Design (CTD) methodologies. We will develop a proof-of-concept tool kit which will allow us to demonstrate and evaluate the above mentioned capabilities. We will compare the performance of our tools with the performance of manual testers and of other model-based tools using comparison criteria that we will define and develop.

cs.SE↗