Search Results (14)

Automated program repair (APR) plays a vital role in enhancing software quality and reducing developer maintenance efforts. Neural Machine Translation (NMT)-based methods demonstrate notable potential by learning translation patterns from bug-fix code pairs. However, traditional approaches are constrained by limited model capacity and training data scale, leading to performance bottlenecks in generalizing to unseen defect patterns. In this paper, we propose CodeTransFix, a novel APR approach that synergistically combines neural machine translation (NMT) methods with code-specific large language models of code (LLMCs) such as CodeBERT. The CodeTransFix approach innovatively learns contextual embeddings of bug-related code through CodeBERT and integrates these representations as supplementary inputs to the Transformer model, enabling context-aware patch generation. The repair performance is evaluated on the widely used Defects4j v1.2 benchmark. Our experimental results showed that CodeTransFix achieved a 54.1% performance improvement compared to the best NMT-based baseline model and a 23.3% performance improvement compared to the best LLMCs for fixing bugs. In addition, CodeTransFix outperformed existing APR methods in the Defects4j v2.0 generalization test. Full article

Automated assessment tools for programming assignments have become increasingly popular in computing education. These tools offer a cost-effective and highly available way to provide timely and consistent feedback to students. However, when evaluating a logically incorrect source code, there are some reasonable concerns about the formative gap in the feedback generated by such tools compared to that of human teaching assistants. A teaching assistant either pinpoints logical errors, describes how the program fails to perform the proposed task, or suggests possible ways to fix mistakes without revealing the correct code. On the other hand, automated assessment tools typically return a measure of the program’s correctness, possibly backed by failing test cases and, only in a few cases, fixes to the program. In this paper, we introduce a tool, AsanasAssist, to generate formative feedback messages to students to repair functionality mistakes in the submitted source code based on the most similar algorithmic strategy solution. These suggestions are delivered with incremental levels of detail according to the student’s needs, from identifying the block containing the error to displaying the correct source code. Furthermore, we evaluate how well the automatically generated messages provided by AsanasAssist match those provided by a human teaching assistant. The results demonstrate that the tool achieves feedback comparable to that of a human grader while being able to provide it just in time. Full article

Portland cement concrete is widely used in road construction due to its durability and minimal maintenance needs. However, its susceptibility to spall highlights the drawbacks of conventional repair methods, including cost inefficiencies, delays, environmental impacts, and safety risks from road closures. To address these challenges, this study evaluated the environmental benefits of a spall detection and repair method employing artificial-intelligence-based computer vision technology. By utilizing machine vision techniques, this approach detects spall damage without road closures and automates the calculation of repair areas and material requirements through a proprietary estimation program. Environmental impact assessments were conducted using life cycle assessment across three frameworks, TRACI, ReCiPe, and ILCD, to compare this method with conventional practices. The results revealed a 79% reduction in the overall environmental impacts, including significant decreases in global warming due to shorter road closures and reduced material waste. Resource usage improved through optimized processes, and air pollution decreased, with lower emissions of smog and particulates. This study highlights the potential of machine-vision-driven repair material quantity takeoff as a more efficient and sustainable alternative. The results of this study will help institutional engineers and practitioners adopt sustainable strategies for green infrastructure repair and integrate them into various infrastructure maintenance practices to contribute to the development of sustainable urban environments. Full article

The substantial value held by smart contracts (SCs) makes them an enticing target for malicious attacks. The process of fixing vulnerabilities in SCs is intricate, primarily due to the immutability of blockchain technology. This research paper introduces a systematic literature review (SLR) that evaluates rectification systems designed to patch vulnerabilities in SCs. Following the guidelines set forth by the PRISMA statement, this SLR meticulously reviews a total of 31 papers. In this context, we classify recently published SC automated repair frameworks based on their methodologies for automatic program repair (APR), rewriting strategies, and tools for vulnerability detection. We argue that automated patching enhances the reliability and adoption of SCs, thereby allowing developers to promptly address identified vulnerabilities. Furthermore, existing automated repair tools are capable of addressing only a restricted range of vulnerabilities, and in some cases, patches may not be effective in preventing the targeted vulnerabilities. Another key point that should be taken into account is the simplicity of the patch and the gas consumption of the modified program. Alternatively, large language models (LLMs) have opened new avenues for automatic patch generation, and their performance can be improved by innovative methodologies. Full article

Over the last two decades, the UAE’s construction sector has grown significantly with the development of tall buildings, but the region faces seismic risks. Similar concerns in China led to earthquake simulation research on a city scale. The objectives include developing programming for parallel computing and creating simplified models for estimating losses. The challenges include computational complexity and uncertainties in various modules. In 1995, the structural engineering community adopted performance-based engineering principles, shifting to a probabilistic design process. The Computational Modeling and Simulation Center (SimCenter) implemented this into a generic software platform, with the 2010 release of Regional Resilience Determination (R2D) automating the methodology. A research plan aims to advance realistic seismic simulation in the UAE, integrating studies and custom developments. The goal is to create an end-to-end seismic risk assessment framework aligned with digital trends, such as BIM and GIS. The investigation focuses on a virtual dataset for tall buildings, considering variations in location, material properties, height, and seismic activity. For the studied archetypes, the average expected losses include a 3.6% collapse probability, a 14% repair cost, 22 days repair time per asset, and almost 1.5% total population injuries, ranging from 1% for the lowest severity to 0.15% for the highest. Full article

In the realm of software development, automated program repair (APR) emerges as a pivotal technique, autonomously debugging faulty code to boost productivity. Despite the notable advancements of large pre-trained language models of code (PLMCs) in code generation, their efficacy in complex tasks like APR remains suboptimal. This limitation is attributed to the generic development of PLMCs, whose specialized potential for APR is yet be to fully explored. In this paper, we propose a novel approach designed to enhance PLMCs’ APR performance through source code augmentation and curriculum learning. Our approach employs code augmentation operators to generate a spectrum of syntactically varied yet semantically congruent bug-fixing programs, thus enriching the dataset’s diversity. Furthermore, we design a curriculum learning strategy, enabling PLMCs to develop a deep understanding of program semantics from these enriched code variants, thereby refining their APR fine-tuning prowess. We apply our approach across different PLMCs and systematically evaluate it on three benchmarks: BFP-small, BFP-medium, and Defects4J. The experimental results show that our approach outperforms both original models and existing baseline methods, demonstrating the promising future of adapting PLMCs for code debugging in practice. Full article

Automatic guided vehicles (AGVs) in the horizontal area play a crucial role in determining the operational efficiency of automated container terminals (ACTs). To improve the operational efficiency of an ACT, it is essential to decrease the impact of battery capacity limitations on AGV scheduling. To address this problem, this paper introduces battery swapping and opportunity charging modes into the AGV system and proposes a new AGV scheduling problem considering the hybrid mode. Firstly, this study describes the AGV scheduling problem of the automated container terminals considering both loading and unloading tasks under the hybrid mode of battery swapping and charging. Thereafter, a mixed-integer programming model is established to minimize the sum of energy costs and delay costs. Secondly, an effective adaptive large neighborhood search algorithm is proposed to solve the problem, in which the initial solution construction, destroy operators, and repair operators are designed according to the hybrid mode. Finally, numerical experiments are conducted to analyze the effectiveness of the model and the optimization performance of the algorithm. The results demonstrate that the hybrid mode of battery swapping and charging can effectively reduce the number of battery swapping times and scheduling costs compared to the existing mode. Full article

Automatic program repair techniques based on deep neural networks have attracted widespread attention from researchers due to the high degree of automation and generality. However, there is a scarcity of high-quality labeled datasets available for training program repair models. This study proposes a method of mining reasonable program repair examples from student program execution logs. Additionally, we introduce the Rookie Simulator (RS), which simulates the error patterns commonly made by novice programmers and generates a large number of program repair sample pairs. To address the issue of low repair rates for infrequent and complex error patterns in compilation errors, the study proposes the attention-enhanced capsule network for program repair (ACNPR), a program repair model that integrates compiler feedback information and utilizes capsule networks to capture complex semantic features. Experimental evaluations were conducted using publicly available datasets, including the DeepFix, TEGCER, and a real course dataset named SUES-COJ mined in this study. The results indicate that our method consistently outperforms current state-of-the-art models in terms of full repair rates. Full article

Although program repair is a tremendous aspect of a software system, it can be extremely challenging. An Automated Program Repair (APR) technique has been proposed to solve this problem. Among them, template-based APR shows good performance. One of the key properties of the template-based APR technique for practical use is its efficiency. However, because the existing techniques mainly focus on performance improvement, they do not sufficiently consider the efficiency. In this study, we propose EffiGenC, which efficiently explores the patch ingredient search space to improve the overall efficiency of the template-based APR. EffiGenC defines the context using the concept of extended reaching definition from compiler theory. EffiGenC constructs the search space by collecting the ingredient required for patching in the context. We evaluated EffiGenC on the Defects4j benchmark. EffiGenC decreases the number of candidate patches from 27% to 86% compared to existing techniques. EffiGenC also correctly/plausibly fixes 47/72 bugs. For Future work, we will solve the search space problem that exists in multiline bugs using context. Full article

Automated program repair is an effective way to ensure software quality and improve software development efficiency. At present, there are many methods and tools of automated program reapir in real world, but most of them have low repair accuracy, resulting in a large number of incorrect patches in the generated patches. To solve this problem, we propose a patch quality evaluation method based on semantic distance, which measures the semantic distance of patches by using features of interval distance, output coverage, and path matching. For each evaluation feature, we give a quantitative formula to obtain a specific distance value and use the distance to calculate the recommended patch value to measure the quality of the patch. Our quality evaluation method evaluated 279 patches from previous program repair tools, including Nopol, DynaMoth, ACS, jGenProg, and CapGen. This quality evaluation method successfully arranged the correct patches before the plausible but incorrect patches, and it recommended the higher-ranked patches to users first. On this basis, we compared our evaluation method with the existing evaluation methods and judged the evaluation ability of each feature. We showed that our proposed patch quality evaluation method can improve the repair accuracy of repair tools. Full article

The alarms of the program-semantic defect-detection report based on static analysis include defects and false positives. The repair of defects and the elimination of false positives are time-consuming and laborious, and new defects may be introduced in the process. To solve these problems, the safe constraints interval of related variables and methods are proposed for the semantic defects in the program, and proposes a functionally equivalent no-side-effect program-semantic defect repair and false-positive elimination strategy based on the test-equivalence theory. This paper realizes the automatic repair of the typical semantic defects of Java programs and the automatic elimination of false positives by adding safe constraint patches. After the repair, the program functions are equivalent and the status of each program point is within the safety range, so that the functions before and after the defect repair are consistent, and the functions and semantics before and after the false positives are eliminated. We have evaluated our approach by repairing 5 projects; our results show that the repair strategy does not require manual confirmation of alarms, automated repair of the program effectively, shortened the repair time greatly, and ensured the correctness of the program after the repair. Full article

Automated methods for detecting defects within composite materials are highly desirable in the drive to increase throughput, optimise repair program effectiveness and reduce component replacement. Tap-testing has traditionally been used for detecting defects but does not provide quantitative measurements, requiring secondary techniques such as ultrasound to certify components. This paper reports on an evaluation of the use of a distributed temperature measurement system—high-definition fibre optic sensing (HD-FOS)—to identify and characterise crushed core and disbond defects in carbon fibre reinforced polymer (CFRP)-skin, aluminium-core, sandwich panels. The objective is to identify these defects in a sandwich panel by measuring the heat transfer through the panel thickness. A heater mat is used to rapidly increase the temperature of the panel with the HD-FOS sensor positioned on the top surface, measuring temperature. HD-FOS measurements are made using the Luna optical distributed sensor interrogator (ODISI) 9100 system comprising a sensor fabricated using standard single mode fibre (SMF)-20 of external diameter 250 $\mathsf{\mu }$m, including the cladding. Results show that areas in which defects are present modulate thermal conductivity, resulting in a lower surface temperature. The resultant data are analysed to identify the length, width and type of defect. The non-invasive technique is amenable to application in challenging operational settings, offering high-resolution visualisation and defect classification. Full article

Automation and autonomous systems are quickly becoming a more engrained aspect of modern society. The need for effective, secure computer code in a timely manner has led to the creation of automated code repair techniques to resolve issues quickly. However, the research to date has largely ignored the human factors aspects of automated code repair. The current study explored trust perceptions, reuse intentions, and trust intentions in code repair with human generated patches versus automated code repair patches. In addition, comments in the headers were manipulated to determine the effect of the presence or absence of comments in the header of the code. Participants were 51 programmers with at least 3 years’ experience and knowledge of the C programming language. Results indicated only repair source (human vs. automated code repair) had a significant influence on trust perceptions and trust intentions. Specifically, participants consistently reported higher levels of perceived trustworthiness, intentions to reuse, and trust intentions for human referents compared to automated code repair. No significant effects were found for comments in the headers. Full article

Phosphorylation of the H2AX protein is an early step in the double strand break (DSB) repair pathway; therefore, phosphorylated histone (γH2AX) foci scoring is widely used as a measure for DSBs. Foci scoring is performed either manually or semi-automatically using hand-operated capturing and image analysis software. In general, both techniques are laborious and prone to artifacts associated with manual scoring. While a few fully automated methods have been described in the literature, none of them have been used to quantify γH2AX foci in combination with a cell cycle phase analysis. Adding this feature to a rapid automated γH2AX foci quantification method would reduce the scoring uncertainty that arises from the variations in the background level of the γH2AX signal throughout the cell cycle. The method was set up to measure DNA damage induced in human mammary epithelial cells by irradiation under a mammogram device. We adapted a FISH (fluorescent in situ hybridization) Spot-counting system, which has a slide loader with automatic scanning and cell capture system throughout the thickness of each cell (z-stack), to meet our assay requirements. While scanning the sample, the system classifies the selected nuclei according to the signal patterns previously described by the user. For our purposes, a double staining immunofluorescence was carried out with antibodies to detect γH2AX and pericentrin, an integral component of the centrosome. We could thus distinguish both the number of γH2AX foci per cell and the cell cycle phase. Furthermore, restrictive settings of the program classifier reduced the “touching nuclei” problem described in other image analysis software. The automated scoring was faster than and as sensitive as its manually performed counterpart. This system is a reliable tool for γH2AX radio-induced foci counting and provides essential information about the cell cycle stage. It thus offers a more complete and rapid assessment of DNA damage. Full article