Test-Case-Driven Programming Understanding in Large Language Models for Better Code Generation

Code generation is to automatically generate source code conforming to a given programming specification, which has received extensive attention especially with the development of large language models (LLMs). Due to the inherent difficulty of code generation, the code generated by LLMs may be also not aligned with the specification. To improve the perfor mance of LLMs in code generation, some Chain of Thought (CoT) techniques have been proposed to guide LLMs for programming understanding before code generation. However, they are still hard to figure out complicated programming logic according to the (concise) specification, leadingto unsatisfactory code generation performance. In this work, we propose the first test-case-driven CoT technique, called TCoT, to further enhance the ability of LLMs in code generation. It understands the programming specification from the novel perspective of test cases, which is aligned with human practice by using examples to understand complicated problems. Due to the existence of the expected output specified in a test case, TCoT can instantly check the correctness of the programming understanding and then refine it to be as correct as possible before code generation. In this way, it is more likely to generate correct code. Our evaluation on 6 datasets and 14 baselines demonstrates the effectiveness of TCoT. For example, TCoT improves ChatGPT by 13.93%~69.44% in terms of Pass@1 (measuring the ratio of programming problems for which the generated code passes all test cases), and outperforms the existing CoT technique with the improvement of 12.14%~53.72% in terms of Pass@1

RFWD3 acts as a tumor promotor in the development and progression of bladder cancer

Background. Bladder cancer is one of the most commonly diagnosed malignancies of the urinary system with relatively poor prognosis and insufficient treatment strategies. RFWD3 is an E3 ligase whose function is rarely investigated in malignant tumors. Methods. A tissue microarray was used for evaluating RFWD3 expression in clinical samples and its correlation with tumor characteristics and patients’ prognosis. RFWD3 knockdown and overexpression cell models were constructed for conducting loss-of-function and gain-of-function assays. qPCR and western blotting were used for detecting mRNA and protein levels of RFWD3, respectively. MTT assay, colony formation assay, flow cytometry, wound-healing assay and transwell assay were carried out to demonstrate the change of cell phenotypes upon RFWD3 knockdown. Results. RFWD3 expression was relatively higher in bladder cancer tissues than in normal tissues, which is correlated with higher N stage and poorer prognosis of patients. Knockdown of RFWD3 in bladder cancer cells significantly inhibited cell proliferation, colony formation, promote cell apoptosis and restrained cell migration. Overexpression of RFWD3 induced the opposite effects. Conclusions. It was illustrated that RFWD3 possesses excellent tumor-promoting ability in bladder cancer. Accordingly, RFWD3 may be a promising therapeutic target in the targeted therapy of bladder cancer, which is worth further research

Code Difference Guided Adversarial Example Generation for Deep Code Models

Adversarial examples are important to test and enhance the robustness of deep code models. As source code is discrete and has to strictly stick to complex grammar and semantics constraints, the adversarial example generation techniques in other domains are hardly applicable. Moreover, the adversarial example generation techniques specific to deep code models still suffer from unsatisfactory effectiveness due to the enormous ingredient search space. In this work, we propose a novel adversarial example generation technique (i.e., CODA) for testing deep code models. Its key idea is to use code differences between the target input (i.e., a given code snippet as the model input) and reference inputs (i.e., the inputs that have small code differences but different prediction results with the target input) to guide the generation of adversarial examples. It considers both structure differences and identifier differences to preserve the original semantics. Hence, the ingredient search space can be largely reduced as the one constituted by the two kinds of code differences, and thus the testing process can be improved by designing and guiding corresponding equivalent structure transformations and identifier renaming transformations. Our experiments on 15 deep code models demonstrate the effectiveness and efficiency of CODA, the naturalness of its generated examples, and its capability of enhancing model robustness after adversarial fine-tuning. For example, CODA reveals 88.05% and 72.51% more faults in models than the state-of-the-art techniques (i.e., CARROT and ALERT) on average, respectively.Comment: Accepted by ASE 202

Effect of sodium aescinate on methyl parathion-induced myocardial injury in rats

Purpose: To explore the effects and mechanism of sodium aescinate (SA) on methyl parathion (MP)-induced myocardial injury.Methods: Rats were divided into following groups: In Control group, rats were administered 0.9 % NaCl by intraperitoneal injection. In MP group, rats were administered 20 mg/kg MP by intraperitoneal injection. In MP + SA group, rats were administered 20 mg/kg MP in combination with SA at a concentration of 0.5, 1.0, or 1.5 mg/kg by intraperitoneal injection. Histological changes were assessed by H&E staining. Serum levels of cardiac troponin T (CTnT) and atrial natriuretie peptide (ANP) were measured by automatic biochemical analyzer and real-time polymerase chain reaction (RT-PCR), respectively. The levels of malondiadehyde (MDA), superoxide dismutase (SOD), glutathione peroxidase (GSH - Px), and glutathione (GSH) in heart tissue was detected by spectrophotometry. The apoptosis of myocardial cells was measured by the terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL) assay. The level of apoptosis-related proteins was assessed by western blot.Results: Superoxide dismutase attenuated MP-induced myocardial injury, and decreased the levels of ANP and cTnT in serum (p < 0.01). Superoxide dismutase attenuated the MP-induced decrease in GSH, GSH-px, and SOD expression (p < 0.05) but increased MDA level (p < 0.01). Moreover, SA inhibited the apoptosis of myocardial cells and regulation of apoptosis-related protein expression (e.g., Bax, Bcl-2, and caspase 3).Conclusion: These results demonstrate that SA attenuates MP-induced myocardial injury by regulating oxidative stress and apoptosis.Keywords: Sodium Aescinate, Methyl Parathion, Acute Organophosphorus Pesticide Poisoning, Myocardial Injur

Lytic cycle: A defining process in oncolytic virotherapy

The viral lytic cycle is an important process in oncolytic virotherapy. Most mathematical models for oncolytic virotherapy do not incorporate this process. In this article, we propose a mathematical model with the viral lytic cycle based on the basic mathematical model for oncolytic virotherapy. The viral lytic cycle is characterized by two parameters, the time period of the viral lytic cycle and the viral burst size. The time period of the viral lytic cycle is modeled as a delay parameter. The model is a nonlinear system of delay differential equations. The model reveals a striking feature that the critical value of the period of the viral lytic cycle is determined by the viral burst size. There are two threshold values for the burst size. Below the first threshold, the system has an unstable trivial equilibrium and a globally stable virus free equilibrium for any nonnegative delay, while the system has a third positive equilibrium when the burst size is greater than the first threshold. When the burst size is above the second threshold, there is a functional relation between the bifurcation value of the delay parameter for the period of the viral lytic cycle and the burst size. If the burst size is greater than the second threshold, the positive equilibrium is stable when the period of the viral lytic cycle is smaller than the bifurcation value, while the system has orbitally stable periodic solutions when the period of the lytic cycle is longer than the bifurcation value. However, this bifurcation value becomes smaller when the burst size becomes bigger. The viral lytic cycle may explain the oscillation phenomena observed in many studies. An important clinic implication is that the burst size should be carefully modified according to its effect on the lytic cycle when a type of a virus is modified for virotherapy, so that the period of the viral lytic cycle is in a suitable range which can break away the stability of the positive equilibria or periodic solutions. (C) 2012 Elsevier Inc. All rights reserved

GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions

Recently, impressive results have been achieved in 3D scene editing with text instructions based on a 2D diffusion model. However, current diffusion models primarily generate images by predicting noise in the latent space, and the editing is usually applied to the whole image, which makes it challenging to perform delicate, especially localized, editing for 3D scenes. Inspired by recent 3D Gaussian splatting, we propose a systematic framework, named GaussianEditor, to edit 3D scenes delicately via 3D Gaussians with text instructions. Benefiting from the explicit property of 3D Gaussians, we design a series of techniques to achieve delicate editing. Specifically, we first extract the region of interest (RoI) corresponding to the text instruction, aligning it to 3D Gaussians. The Gaussian RoI is further used to control the editing process. Our framework can achieve more delicate and precise editing of 3D scenes than previous methods while enjoying much faster training speed, i.e. within 20 minutes on a single V100 GPU, more than twice as fast as Instruct-NeRF2NeRF (45 minutes -- 2 hours).Comment: Project page: https://GaussianEditor.github.i

Fuzzing Deep Learning Compilers with HirGen

Deep Learning (DL) compilers are widely adopted to optimize advanced DL models for efficient deployment on diverse hardware. Their quality has profound effect on the quality of compiled DL models. A recent bug study shows that the optimization of high-level intermediate representation (IR) is the most error-prone compilation stage. Bugs in this stage are accountable for 44.92% of the whole collected ones. However, existing testing techniques do not consider high-level optimization related features (e.g. high-level IR), and are therefore weak in exposing bugs at this stage. To bridge this gap, we propose HirGen, an automated testing technique that aims to effectively expose coding mistakes in the optimization of high-level IR. The design of HirGen includes 1) three coverage criteria to generate diverse and valid computational graphs; 2) full use of high-level IRs language features to generate diverse IRs; 3) three test oracles inspired from both differential testing and metamorphic testing. HirGen has successfully detected 21 bugs that occur at TVM, with 17 bugs confirmed and 12 fixed. Further, we construct four baselines using the state-of-the-art DL compiler fuzzers that can cover the high-level optimization stage. Our experiment results show that HirGen can detect 10 crashes and inconsistencies that cannot be detected by the baselines in 48 hours. We further validate the usefulness of our proposed coverage criteria and test oracles in evaluation