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1. Directed Test Generation for Activation of Security Assertions in RTL Models

   https://doi.org/10.1145/3441297  

   Witharana, Hasini ; Lyu, Yangdi ; Mishra, Prabhat ( April 2021 , ACM Transactions on Design Automation of Electronic Systems)

   null (Ed.)

   Assertions are widely used for functional validation as well as coverage analysis for both software and hardware designs. Assertions enable runtime error detection as well as faster localization of errors. While there is a vast literature on both software and hardware assertions for monitoring functional scenarios, there is limited effort in utilizing assertions to monitor System-on-Chip (SoC) security vulnerabilities. We have identified common SoC security vulnerabilities and defined several classes of assertions to enable runtime checking of security vulnerabilities. A major challenge in assertion-based validation is how to activate the security assertions to ensure that they are valid. While existing test generation using model checking is promising, it cannot generate directed tests for large designs due to state space explosion. We propose an automated and scalable mechanism to generate directed tests using a combination of symbolic execution and concrete simulation of RTL models. Experimental results on diverse benchmarks demonstrate that the directed tests are able to activate security assertions non-vacuously. 

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2. Scalable Concolic Testing of RTL Models

   https://doi.org/10.1109/TC.2020.2997644  

   Lyu, Yangdi ; Mishra, Prabhat ( January 2020 , IEEE Transactions on Computers)

   Simulation is widely used for validation of Register-Transfer-Level (RTL) models. While simulating with millions of random or constrained-random tests can cover majority of the functional scenarios, the number of remaining scenarios can still be huge (hundreds or thousands) in case of today's industrial designs. Hard-to-activate branches are one of the major contributors for such remaining/untested scenarios. While directed test generation techniques using formal methods are promising in activating branches, it is infeasible to apply them on large designs due to state space explosion. In this paper, we propose a fully automated and scalable approach to cover the hard-to-activate branches using concolic testing of RTL models. While application of concolic testing on hardware designs has shown some promising results in improving the overall coverage, they are not designed to activate specific targets such as uncovered corner cases and rare scenarios. This paper makes two important contributions. (1) We propose a directed test generation technique to activate a target by effective utilization of concolic testing on RTL models. (2) We develop efficient learning and clustering techniques to minimize the overlapping searches across targets to drastically reduce the overall test generation effort. 

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3. Route : Roads Not Taken in UI Testing

   https://doi.org/10.1145/3571851  

   Lin, Jun-Wei ; Salehnamadi, Navid ; Malek, Sam ( July 2023 , ACM Transactions on Software Engineering and Methodology)

   Core features (functionalities) of an app can often be accessed and invoked in several ways, i.e., through alternative sequences of user-interface (UI) interactions. Given the manual effort of writing tests, developers often only consider the typical way of invoking features when creating the tests (i.e., the “sunny day scenario”). However, the alternative ways of invoking a feature are as likely to be faulty. These faults would go undetected without proper tests. To reduce the manual effort of creating UI tests and help developers more thoroughly examine the features of apps, we presentRoute, an automated tool for feature-based UI test augmentation for Android apps.Routefirst takes a UI test and the app under test as input. It then applies novel heuristics to find additional high-quality UI tests, consisting of both inputs and assertions, that verify the same feature as the original test in alternative ways. Application ofRouteon several dozen tests for popular apps on Google Play shows that for 96% of the existing tests,Routewas able to generate at least one alternative test. Moreover, the fault detection effectiveness of augmented test suites in our experiments showed substantial improvements of up to 39% over the original test suites.

    

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4. Avoiding help avoidance: Using interface design changes to promote unsolicited hint usage in an intelligent tutor.

   https://doi.org/10.1007/s40593-020-00213-3  

   Maniktala, M. ; Cody, C. ; Barnes, T. ; & Chi, M. ( January 2020 , Journal of artificial intelligence in education)

   null (Ed.)

   Within intelligent tutoring systems, considerable research has in-vestigated hints, including how to generate data-driven hints, what hint con-tent to present, and when to provide hints for optimal learning outcomes. How-ever, less attention has been paid to how hints are presented. In this paper, we propose a new hint delivery mechanism called “Assertions” for providing unsolicited hints in a data-driven intelligent tutor. Assertions are partially-worked example steps designed to appear within a student workspace, and in the same format as student-derived steps, to show students a possible subgoal leading to the solution. We hypothesized that Assertions can help address the well-known hint avoidance problem. In systems that only provide hints upon request, hint avoidance results in students not receiving hints when they are needed. Our unsolicited Assertions do not seek to improve student help-seeking, but rather seek to ensure students receive the help they need. We contrast Assertions with Messages, text-based, unsolicited hints that appear after student inactivity. Our results show that Assertions significantly increase unsolicited hint usage compared to Messages. Further, they show a signifi-cant aptitude-treatment interaction between Assertions and prior proficiency, with Assertions leading students with low prior proficiency to generate shorter (more efficient) posttest solutions faster. We also present a clustering analysis that shows patterns of productive persistence among students with low prior knowledge when the tutor provides unsolicited help in the form of Assertions. Overall, this work provides encouraging evidence that hint presentation can significantly impact how students use them and using Assertions can be an effective way to address help avoidance. 

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5. Statistical assertions for validating patterns and finding bugs in quantum programs

   https://doi.org/10.1145/3307650.3322213  

   Huang, Yipeng ; Martonosi, Margaret ( June 2019 , Proceedings of the 46th International Symposium on Computer Architecture (ISCA ’19))

   In support of the growing interest in quantum computing experimentation, programmers need new tools to write quantum algorithms as program code. Compared to debugging classical programs, debugging quantum programs is difficult because programmers have limited ability to probe the internal states of quantum programs; those states are difficult to interpret even when observations exist; and programmers do not yet have guidelines for what to check for when building quantum programs. In this work, we present quantum program assertions based on statistical tests on classical observations. These allow programmers to decide if a quantum program state matches its expected value in one of classical, superposition, or entangled types of states. We extend an existing quantum programming language with the ability to specify quantum assertions, which our tool then checks in a quantum program simulator. We use these assertions to debug three benchmark quantum programs in factoring, search, and chemistry. We share what types of bugs are possible, and lay out a strategy for using quantum programming patterns to place assertions and prevent bugs. 

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