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Large Language Models (LLMs) have received much recent attention due to their human-level accuracy. While existing works mostly focus on either improving accuracy or testing accuracy robustness, the computation efficiency of LLMs, which is of paramount importance due to often vast generation demands and real-time requirements, has surprisingly received little attention. In this article, we make the first attempt to understand and test potential computation efficiency robustness in state-of-the-art LLMs. By analyzing the working mechanism and implementation of 20,543 public-accessible LLMs, we observe a fundamental property in LLMs that could be manipulated in an adversarial manner to reduce computation efficiency significantly. Our interesting observation is that the output length determines the computation efficiency of LLMs instead of the input, where the output length depends on two factors: an often sufficiently large yet pessimistic pre-configured threshold controlling the max number of iterations and a runtime-generated end of sentence (EOS) token. Our key motivation is to generate test inputs that could sufficiently delay the generation of EOS such that LLMs would have to go through enough iterations to satisfy the pre-configured threshold. We present LLMEffiChecker, which can work under both white-box setting and black-box setting. In the white-box scenario, LLMEffiChecker develops a gradient-guided technique that searches for a minimal and unnoticeable perturbation at character-level, token-level, and structure-level. In the black-box scenario, LLMEffiChecker employs a causal inference-based approach to find critical tokens and similarly applies three levels of imperceptible perturbation to them. Both the white-box and black-box settings effectively delay the appearance of EOS, compelling these inputs to reach the naturally unreachable threshold. To demonstrate the effectiveness of LLMEffiChecker, we conduct a systematic evaluation on nine publicly available LLMs: Google T5, AllenAI WMT14, Helsinki-NLP translator, Facebook FairSeq, UNICAMP-DL translator, MarianMT, Google FLAN-T5, MBZUAI LaMini-GPT, and Salesforce CodeGen. Experimental results show that LLMEffiChecker can increase on average LLMs' response latency and energy consumption by 325% to 3,244% and 344% to 3,616%, respectively, by perturbing just one character or token in the input sentence. Our case study shows that inputs generated by LLMEffiChecker significantly affect the battery power in real-world mobile devices (i.e., drain more than 30 times battery power than normal inputs). [ABSTRACT FROM AUTHOR]

Intelligent deep learning-based models have made significant progress for automated source code semantics embedding, and current research works mainly leverage natural language-based methods and graph-based methods. However, natural language-based methods do not capture the rich semantic structural information of source code, and graph-based methods do not utilize rich distant information of source code due to the high cost of message-passing steps. In this article, we propose a novel interpretable model, called graph tensor convolution neural network (GTCN), to generate accurate code embedding, which is capable of comprehensively capturing the distant information of code sequences and rich code semantics structural information. First, we propose to utilize a high-dimensional tensor to integrate various heterogeneous code graphswith node sequence features, such as control flow, data flow. Second, inspired by the current advantages of graph-based deep learning and efficient tensor computations, we propose a novel interpretable graph tensor convolution neural network for learning accurate code semantic embedding from the code graph tensor. Finally, we evaluate three popular applications on the GTCN model: variable misuse detection, source code prediction, and vulnerability detection. Compared with current state-of-the-art methods, our model achieves higher scores with respect to the top-1 accuracy while costing less training time. [ABSTRACT FROM AUTHOR]

Background. Code-line-level bugginess identification (CLBI) is a vital technique that can facilitate developers to identify buggy lines without expending a large amount of human effort. Most of the existing studies tried to mine the characteristics of source codes to train supervised prediction models, which have been reported to be able to discriminate buggy code lines amongst others in a target program. Problem. However, several simple and clear code characteristics, such as complexity of code lines, have been disregarded in the current literature. Such characteristics can be acquired and applied easily in an unsupervised way to conduct more accurate CLBI, which also can decrease the application cost of existing CLBI approaches by a large margin. Objective. We aim at investigating the status quo in the field of CLBI from the perspective of (1) how far we have really come in the literature, and (2) how far we have yet to go in the industry, by analyzing the performance of state-of-the-art (SOTA) CLBI approaches and tools, respectively. Method. We propose a simple heuristic baseline solution GLANCE (aiminG at controL- ANd ComplEx-statements) with three implementations (i.e., GLANCE-MD, GLANCE-EA, and GLANCE-LR). GLANCE is a two-stage CLBI framework: first, use a simple model to predict the potentially defective files; second, leverage simple code characteristics to identify buggy code lines in the predicted defective files. We use GLANCE as the baseline to investigate the effectiveness of the SOTA CLBI approaches, including natural language processing (NLP) based, model interpretation techniques (MIT) based, and popular static analysis tools (SAT). Result. Based on 19 open-source projects with 142 different releases, the experimental results show that GLANCE framework has a prediction performance comparable or even superior to the existing SOTA CLBI approaches and tools in terms of 8 different performance indicators. Conclusion. The results caution us that, if the identification performance is the goal, the real progress in CLBI is not being achieved as it might have been envisaged in the literature and there is still a long way to go to really promote the effectiveness of static analysis tools in industry. In addition, we suggest using GLANCE as a baseline in future studies to demonstrate the usefulness of any newly proposed CLBI approach. [ABSTRACT FROM AUTHOR]