arXiv subjects

Gregg Rothermel

Publications and source records attributed to Gregg Rothermel.

4 recordsLinked to original sources

arXiv2026

Flaky Test Recognition when Testing CPSs Using Hybrid Models

Cyber-Physical Systems (CPSs) have many applications, ranging from simple thermostat systems to autonomous driving systems and medical devices. Like all systems, they need to function correctly. To this end, validation techniques such as testing that can effectively reveal faults are required. Also, CPSs usually operate in uncertain environments where various expected/unexpected events can affect their behaviors. These events together with timing and synchronization incidents may result in various/different CPS behaviors and may cause a CPS to pass a test under some conditions and fail it under others. Such test cases are called ``flaky'' test cases and we call the conditions that are responsible for them ``flaky conditions'' and call these behaviors ``flaky behaviors''. When test cases are flaky, testing results are unreliable. To achieve more reliable test results, engineers may attempt to recognize flaky test cases and remove them. In this work, beginning with a test case generation and execution technique called HyTest that we created previously, we integrate TReVa, a new technique that validates testing results provided by HyTest and FlaRe, a new technique that recognizes flaky test cases and flaky conditions using hybrid models during the early stages of CPS development in just one additional round of testing. We present the results of an empirical study evaluating the effectiveness of our new approach (which we call HyTestTF). Our results show that correctly differentiate flaky test cases from non-flaky test cases

cs.SE↗

arXiv2023

Test Case Generation and Test Oracle Support for Testing CPSs using Hybrid Models

Cyber-Physical Systems (CPSs) play a central role in the behavior of a wide range of autonomous physical systems such as medical devices, autonomous vehicles, and smart homes, many of which are safety-critical. CPSs are often specified iteratively as a sequence of models at different levels that can be tested via simulation systems at early stages of their development cycle. One such model is a hybrid automaton; these are used frequently for CPS applications and have the advantage of encapsulating both continuous and discrete CPS behaviors. When testing CPSs, engineers can take advantage of these models to generate test cases that target both types of these behaviors. Moreover, since these models are constructed early in the development process for CPSs, they allow test cases to be generated early in that process for those CPSs, even before simulation models of the CPSs have been designed. One challenge when testing CPSs is that these systems may operate differently even under an identically applied test scenario. In such cases, we cannot employ test oracles that use predetermined deterministic behaviors; instead, test oracles should consider sets of desired behaviors in order to determine whether the CPS has behaved appropriately. In this paper we present a test case generation technique, HYTEST, that generates test cases based on hybrid models, accompanied by appropriate test oracles, for use in testing CPSs early in their development cycle. To evaluate the effectiveness and efficiency of HYTEST, we conducted an empirical study in which we applied the technique to several CPSs and measured its ability to detect faults in those CPSs and the amount of time required to perform the testing process. The results of the study show that HYTEST was able to detect faults more effectively and efficiently than the baseline techniques we compare it to.

cs.SE↗

arXiv2022

Assessing Expert System-Assisted Literature Reviews With a Case Study

Given the large number of publications in software engineering, frequent literature reviews are required to keep current on work in specific areas. One tedious work in literature reviews is to find relevant studies amongst thousands of non-relevant search results. In theory, expert systems can assist in finding relevant work but those systems have primarily been tested in simulations rather than in application to actual literature reviews. Hence, few researchers have faith in such expert systems. Accordingly, using a realistic case study, this paper assesses how well our state-of-the-art expert system can help with literature reviews. The assessed literature review aimed at identifying test case prioritization techniques for automated UI testing, specifically from 8,349 papers on IEEE Xplore. This corpus was studied with an expert system that incorporates an incrementally updated human-in-the-loop active learning tool. Using that expert system, in three hours, we found 242 relevant papers from which we identified 12 techniques representing the state-of-the-art in test case prioritization when source code information is not available. These results were then validated by six other graduate students manually exploring the same corpus. Without the expert system, this task would have required 53 hours and would have found 27 additional papers. That is, our expert system achieved 90% recall with 6% of the human effort cost when compared to a conventional manual method. Significantly, the same 12 state-of-the-art test case prioritization techniques were identified by both the expert system and the manual method. That is, the 27 papers missed by the expert system would not have changed the conclusion of the literature review. Hence, if this result generalizes, it endorses the use of our expert system to assist in literature reviews.

cs.SE↗

arXiv2019

TERMINATOR: Better Automated UI Test Case Prioritization

Automated UI testing is an important component of the continuous integration process of software development. A modern web-based UI is an amalgam of reports from dozens of microservices written by multiple teams. Queries on a page that opens up another will fail if any of that page's microservices fails. As a result, the overall cost for automated UI testing is high since the UI elements cannot be tested in isolation. For example, the entire automated UI testing suite at LexisNexis takes around 30 hours (3-5 hours on the cloud) to execute, which slows down the continuous integration process. To mitigate this problem and give developers faster feedback on their code, test case prioritization techniques are used to reorder the automated UI test cases so that more failures can be detected earlier. Given that much of the automated UI testing is "black box" in nature, very little information (only the test case descriptions and testing results) can be utilized to prioritize these automated UI test cases. Hence, this paper evaluates 17 "black box" test case prioritization approaches that do not rely on source code information. Among these, we propose a novel TCP approach, that dynamically re-prioritizes the test cases when new failures are detected, by applying and adapting a state of the art framework from the total recall problem. Experimental results on LexisNexis automated UI testing data show that our new approach (which we call TERMINATOR), outperformed prior state of the art approaches in terms of failure detection rates with negligible CPU overhead.

cs.SE↗