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Large Language models have achieved impressive performance in automated software engineering. Extensive efforts have been made to evaluate the abilities of code LLMs in various aspects, with an increasing number of benchmarks and evaluation frameworks proposed. Apart from the most sought-after capability of code generation, the capability of code comprehension is being granted growing attention. Nevertheless, existing works assessing the code comprehension capability of LLMs exhibit varied limitations. Evaluation frameworks like CRUXEval and REval usually focus on code reasoning tasks over a certain input case, leading to a limited range of execution traces covered, resulting in a loss in code semantics examined and the inability to assess the comprehensive understanding of LLMs concerning the target program. To tackle the challenges above, we propose SpecEval, a novel black-box evaluation framework to evaluate code comprehension in LLMs via program specifications. Inspired by the idea that specifications can comprehensively articulate program behaviors concerning all possible execution traces, we employ formal specifications to represent program semantics and perform thorough evaluations. In particular, four specification-related tasks are designed to assess the capability of LLMs from basic to advanced levels. Moreover, counterfactual analysis is conducted to study the performance variance of LLMs under semantics-preserving perturbations, and progressive consistency analysis is performed to study the performance consistency of LLMs over a series of tasks with sequential dependence. Systematic experiments are conducted on six state-of-the-art LLMs. Experimental results present a below-satisfactory performance of LLMs on specification-related tasks, revealing the limitations of existing LLMs in articulating program semantics, underscoring future directions for enhancement.

The flourishing ecosystem centered around voice personal assistants (VPA), such as Amazon Alexa, has led to the booming of VPA apps. The largest app market Amazon skills store, for example, hosts over 200,000 apps. Despite their popularity, the open nature of app release and the easy accessibility of apps also raise significant concerns regarding security, privacy and quality. Consequently, various testing approaches have been proposed to systematically examine VPA app behaviors. To tackle the inherent lack of a visible user interface in the VPA app, two strategies are employed during testing, i.e., chatbot-style testing and model-based testing. The former often lacks effective guidance for expanding its search space, while the latter falls short in interpreting the semantics of conversations to construct precise and comprehensive behavior models for apps. In this work, we introduce Elevate, a model-enhanced large language model (LLM)-driven VUI testing framework. Elevate leverages LLMs' strong capability in natural language processing to compensate for semantic information loss during model-based VUI testing. It operates by prompting LLMs to extract states from VPA apps' outputs and generate context-related inputs. During the automatic interactions with the app, it incrementally constructs the behavior model, which facilitates the LLM in generating inputs that are highly likely to discover new states. Elevate bridges the LLM and the behavior model with innovative techniques such as encoding behavior model into prompts and selecting LLM-generated inputs based on the context relevance. Elevate is benchmarked on 4,000 real-world Alexa skills, against the state-of-the-art tester Vitas. It achieves 15% higher state space coverage compared to Vitas on all types of apps, and exhibits significant advancement in efficiency., Comment: 13 pages, 11 figures

The rise of code pre-trained models has significantly enhanced various coding tasks, such as code completion, and tools like GitHub Copilot. However, the substantial size of these models, especially large models, poses a significant challenge when it comes to fine-tuning them for specific downstream tasks. As an alternative approach, retrieval-based methods have emerged as a promising solution, augmenting model predictions without the need for fine-tuning. Despite their potential, a significant challenge is that the designs of these methods often rely on heuristics, leaving critical questions about what information should be stored or retrieved and how to interpolate such information for augmenting predictions. To tackle this challenge, we first perform a theoretical analysis of the fine-tuning process, highlighting the importance of delta logits as a catalyst for improving model predictions. Building on this insight, we develop a novel retrieval-based method, FT2Ra, which aims to mimic genuine fine-tuning. While FT2Ra adopts a retrieval-based mechanism, it uniquely adopts a paradigm with a learning rate and multi-epoch retrievals, which is similar to fine-tuning.In token-level completion, which represents a relatively easier task, FT2Ra achieves a 4.29% improvement in accuracy compared to the best baseline method on UniXcoder. In the more challenging line-level completion task, we observe a substantial more than twice increase in Exact Match (EM) performance, indicating the significant advantages of our theoretical analysis. Notably, even when operating without actual fine-tuning, FT2Ra exhibits competitive performance compared to the models with real fine-tuning., Comment: ISSTA 2024

Formal program specifications play a crucial role in various stages of software development. However, manually crafting formal program specifications is rather difficult, making the job time-consuming and labor-intensive. It is even more challenging to write specifications that correctly and comprehensively describe the semantics of complex programs. To reduce the burden on software developers, automated specification generation methods have emerged. However, existing methods usually rely on predefined templates or grammar, making them struggle to accurately describe the behavior and functionality of complex real-world programs. To tackle this challenge, we introduce SpecGen, a novel technique for formal program specification generation based on Large Language Models. Our key insight is to overcome the limitations of existing methods by leveraging the code comprehension capability of LLMs. The process of SpecGen consists of two phases. The first phase employs a conversational approach that guides the LLM to generate appropriate specifications for a given program. The second phase, designed for where the LLM fails to generate correct specifications, applies four mutation operators to the model-generated specifications and selects verifiable specifications from the mutated ones through a novel heuristic selection strategy. We evaluate SpecGen on two datasets, including the SV-COMP Java category benchmark and a manually constructed dataset. Experimental results demonstrate that SpecGen succeeds in generating verifiable specifications for 279 out of 385 programs, outperforming the existing purely LLM-based approaches and conventional specification generation tools like Houdini and Daikon. Further investigations on the quality of generated specifications indicate that SpecGen can comprehensively articulate the behaviors of the input program.

Numerous methods for crafting adversarial examples were proposed recently with high success rate. Since most existing machine learning based classifiers normalize images into some continuous, real vector, domain firstly, attacks often craft adversarial examples in such domain. However, "adversarial" examples may become benign after denormalizing them back into the discrete integer domain, known as the discretization problem. This problem was mentioned in some work, but has received relatively little attention. In this work, we first conduct a comprehensive study of existing methods and tools for crafting. We theoretically analyze 34 representative methods and empirically study 20 representative open source tools for crafting adversarial images. Our study reveals that the discretization problem is far more serious than originally thought. This suggests that the discretization problem should be taken into account seriously when crafting adversarial examples and measuring attack success rate. As a first step towards addressing this problem in black-box scenario, we propose a black-box method which reduces the adversarial example searching problem to a derivative-free optimization problem. Our method is able to craft adversarial images by derivative-free search in the discrete integer domain. Experimental results show that our method is comparable to recent white-box methods (e.g., FGSM, BIM and C\&W) and achieves significantly higher success rate in terms of adversarial examples in the discrete integer domain than recent black-box methods (e.g., ZOO, NES-PGD and Bandits). Moreover, our method is able to handle models that is non-differentiable and successfully break the winner of NIPS 2017 competition on defense with 95\% success rate. Our results suggest that discrete optimization algorithms open up a promising area of research into effective black-box attacks.

Communication Based Train Control (CBTC) system is the state-of-the-art train control system. In a CBTC system, to guarantee the safety of train operation, trains communicate with each other intensively and adjust their control modes autonomously by computing critical control parameters, e.g. velocity range, according to the information they get. As the correctness of the control parameters generated are critical to the safety of the system, a method to verify these parameters is a strong desire in the area of train control system. In this paper, we present our ideas of how to model and verify the control parameter calculations in a CBTC system efficiently. - As the behavior of the system is highly nondeterministic, it is difficult to build and verify the complete behavior space model of the system online in advance. Thus, we propose to model the system according to the ongoing behavior model induced by the control parameters. - As the parameters are generated online and updated very quickly, the verification result will be meaningless if it is given beyond the time bound, since by that time the model will be changed already. Thus, we propose a method to verify the existence of certain dangerous scenarios in the model online quickly. To demonstrate the feasibility of these proposed approaches, we present the composed linear hybrid automata with readable shared variables as a modeling language to model the control parameters calculation and give a path-oriented reachability analysis technique for the scenario-based verification of this model. We demonstrate the model built for the CBTC system, and show the performance of our technique in fast online verification. Last but not least, as CBTC system is a typical CPS system, we also give a short discussion of the potential directions for CPS verification in this paper., Comment: 16 pages, 4 figures