Integrating Large Language Models (LLMs) into software fault localization represents a significant advancement in improving debugging efficiency for programmers. However, novice program fault localization, which is essential for computer science education, has not been thoroughly investigated in previous studies. In contrast to industrial programs target practical functionality, novice programs primarily deal with individual algorithmic issues. The distinct logic structures between novice and industrial programs can impact how effectively LLM understand and process them. Moreover, this difference reveals the inapplicability of the Competent Programmer Hypothesis, a fundamental assumption in industrial fault localization, to novice program fault localization. Therefore, industrial methodologies are unsuitable for novice programming, emphasizing the need for our empirical studies. To fill this gap, we evaluate LLMs’ effectiveness in localizing faults for novice programs in statement level. Using the widely used novice programs dataset Codeflaws and Condefects, we compare the performance of two commercial LLMs (i.e., ChatGPT-3.5 and ChatGPT-4) and three open-source LLMs (i.e., ChatGLM3, Llama2, and Code Llama) against traditional fault localization methods, examining their accuracy and overlap. Additionally, we investigate how prompt engineering improves localization precision. Our findings show ChatGPT-4’s overall superior performance, with ChatGPT-3.5 exhibiting minor advantages in certain cases. ChatGPT-4 outperforms the traditional methods with best performance by 592% and 137% on Codeflaws and Condefects. Specifically, each method exhibits unique strengths in localizing novice programming faults. Moreover, carefully crafted prompts can improve LLMs’ precision. These insights underscore the promising potential of utilizing LLMs for fault localization in novice programming.

Summary Online judgment (OJ) systems are capable of evaluating program results by automatically executing test cases, significantly improving the efficiency of traditional guidance approaches. Moreover, existing studies attempt to assist novices through automated fault localization techniques to provide feedback to novices, which can help them quickly find the location of faulty statements. Among them, spectrum‐based fault localization (SBFL) techniques have been widely used for their lightweight and efficiency, which only requires coverage information and test results of test cases to conduct fault localization. However, manually constructing high‐quality test cases for a large number of OJ questions is tough work to complete. To solve this problem, we propose the novice program‐orientedMulti‐Objective Optimization‐BasedFault Localization‐OrientedTestCaseGeneration (MFTCG) for automatically generating test inputs. Specifically, we use multi‐objective optimization algorithms to evolve the test case in terms of both fault localization and faulty code detection capability. We conduct experiments with 8911 programs from the well‐known public OJ platform AtCoder. The results show that our proposed approach MFTCG can achieve the best fault localization performance compared with existing automated test case generation approaches in most cases and can achieve the similar faulty code detection capability compared to manually designed test cases.

Spectrum-Based Fault Localization (SBFL) technique is widely applied for fault localization, identifying faulty statements potentially resulting in unexpected faulty programs’ behavior. However, researchers have approved that Coincidental Correct (CC) test cases contained in test suites can negatively affect the accuracy of SBFL. Previous researchers sought to identify CC test cases through machine learning algorithms, but the feature representation is insufficient, leading to limited accuracy. To address this challenge, we propose the Machine Learning-based CC test cases Identification approach (MLCCI), which leverages multiple features extracted from the program under test to identify CC test cases and map the CC identification task to a learning problem. To evaluate the performance of MLCCI, we conduct experiments in the well-known dataset Defects4J. The experimental results compared with state-of-the-art baselines indicate that: (1) MLCCI achieves higher CC identifying accuracy, with the average Recall, P recision, and F -measure values of MLCCI are 65.93%, 71.69%, and 53.74%, respectively; (2) The fault localization accuracy of MLCCI with the Jaccard formula outperforms baselines, where the values of Accuracy@ 1, 3, and 5 are 347, 369, and 393, achieving the maximum 137.67%, 67.73%, and 47.74% improvement against baselines, respectively. Besides, we perform ablation analysis to reveal the effectiveness of features utilized in this study.

A CMOS technology compatible non-volatile SRAM (cNV SRAM) is proposed in this paper to achieve energy efficient on-chip memory. cNV SRAM works as conventional 8T SRAM to keep high speed in work mode; in sleep mode, it backs up the data in its NV component and switches off the power supply, thereby minimizing the leakage energy without data loss. The circuit- and architectural- level implementation schemes of cNV SRAM are developed considering multiple key performance parameters including energy dissipation, access time, write time, noise margin, layout area, restoration time, and injection charges. Simulation results on SPEC 2000 benchmark suite demonstrate that cNV SRAM realizes 86 percent energy savings on average with negligible performance impact and small hardware overhead as compared to conventional SRAM. Finally, the impact of the sleep time and memory size on the effectiveness of cNV SRAM is analyzed in detail and it shows that cNV SRAM is particularly effective to implement large on-chip memories with long idle time.