Search Results (19)

Some knowledge bases (KBs) extracted from Wikipedia articles can achieve very high average precision values (over 95% in DBpedia). However, subtle mistakes including inconsistencies, outliers, and erroneous relations are usually ignored in the construction of KBs by extraction rules. Automatic detection and correction of these subtle errors is important for improving the quality of KBs. In this paper, an inductive logic programming with exceptional information (EILP) is proposed to automatically detect errors in large knowledge bases (KBs). EILP leverages the exceptional information problems that are ignored in conventional rule-learning algorithms such as inductive logic programming (ILP). Furthermore, an inductive logical correction method with exceptional features (EILC) is proposed to automatically correct these mistakes by learning a set of correction rules with exceptional features, in which respective metrics are provided to validate the revised triples. The experimental results demonstrate the effectiveness of EILP and EILC in detecting and repairing large knowledge bases, respectively. 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

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

Due to the explosive growth of software quantity and the mixed ability of software developers, a large number of software defects emerge during the later stages of software maintenance. The search method based on genetic programming is one of the most popular in search algorithms, but it also has some issues. The single-objective approach to validate and select offspring patches without considering other constraints can affect the efficiency of patch generation. To address this issue, this paper proposes an automatic software repair method based on Multi-objective Genetic Programming (MGPRepair). Firstly, the method adopts a lightweight context analysis strategy to find suitable repair materials. Secondly, it decouples the replacement statements and insertion statements in the repair materials, using a lower-granularity patch representation method to encode the patches in the search space. Then, the automatic software defect repair is treated as a multi-objective search problem, and the NSGA-II multi-objective optimization algorithm is used to find simpler repair patches. Finally, the test case filtering technique is used to accelerate the patch validation process and generate correct patches. MGPRepair was experimentally evaluated on 395 real Java software defects from the Defects4J dataset. The experimental results show that MGPRepair can generate test case-passing patches for 51 defects, of which 35 defect patches are equivalent to manually generated patches. Its repair the efficiency and success rate are higher to other excellent automatic software defect repair methods such as jGenProg, RSRepair, ARJA, Nopol, Capgen, and SequenceR. 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

In order to maintain a suitable road pavement level with limited resources, a management system must be established. In order to achieve this goal, a program using AI (artificial intelligence) was developed to manage and evaluate a sizable volume of survey data. A national highway pavement data management system (PDMS) built on the WEB was also constructed. By connecting several artificial neural networks, the AI crack analysis algorithm was created and taught to automatically recognize cracks in road photos and calculate crack rates. In the PDMS, the current condition of a national highway can be shown on a map, and all the data are updated to allow for verification in increments of 100 m for each lane. The system was also improved to enable the collection of information on the detailed survey section’s pavement repair specifics according to survey year. 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

Automatic program repair has drawn more and more attention since software quality is facing increasing challenges. In existing approaches, the unlimited search space is considered to be the main limitation in finding the correct patch. So how to reduce the search space to improve the efficiency of automatic program repair remains a problem to be solved. In this work, we represent a similarity-based and location-awareness-based automatic program repair (SLARepair). SLARepair takes the similarity between codes as important search information. The search space is further subdivided by the location-awareness strategy to improve search efficiency. In addition, to better guide the search process, a new fitness function is designed for genetic programming, which brings notable improvements. Moreover, the patch verification time is further reduced by utilizing the test case prioritization approach combined with test case filtering. Extensive experiments demonstrate that our SLARepair outperforms the state-of-the-art approaches on the Defects4J benchmark and achieves competitive performances. Full article

This study investigates the scheduling of mechanical repairs performed at a Portuguese firm in the automobile sector. The aim is to reduce the amount of time that vehicles spend inactive between interventions by developing a mathematical model that takes into account the available resources and mechanics, the necessary interventions, and the time required for each repair. To accomplish this, a mixed-integer linear programming (MILP) model was employed, incorporating various variables to schedule interventions, allocate resources, and determine start times for each vehicle. The problem was formulated using the AMPL modeling language, and real-world instances of the problem, derived from data provided by the company, were solved using the Gurobi solver. Results show that the developed model significantly improves the scheduling of the vehicles’ repairs at the firm, leading to a reduction of 67% on average in the downtime of the vehicles and allowing an automatic correct schedule of the mechanical interventions. Moreover, the comparison of the scheduling obtained from the developed model and the firm’s procedure shows that interventions on vehicles arriving at the repair shop are mostly repaired on the day of entry, allowing for quicker delivery to the customer. Full article

In order to improve the automatic level for laser-cladding repair of high value industrial equipment, such as polycrystalline diamond compact bit (PDC bit) applied in oil industry, a universal scheme of integrated automatic system for repairing is proposed in this paper, and the basic functional modules together with the executing order according to which each module runs are defined. There are two main technical points, i.e.,inspection and repairing, that need to be realized for such integrated automatic system. Therefore, according to the proposed scheme and the existing instruments, a dual-robot system, which includes two KUKA industrial robots, is adopted as the technological implementation, where one robot is used to carry a 3D scanner to reconstruct the PDC bit to realize inspection while the other is used to hold the laser to melt the special powder flowing to the damaged region of the bit to complete cladding. To realize automatic running of the whole integrated system, a hand-eye calibration method, namely three-point calibration, is then proposed, by which coordinates of point cloud of the damaged PDC bit detected by 3D scanner can be transformed to those of the coordinate system of the robot with the laser, so that the cladding path planned via cutting slice of the damaged region of the PDC bit in the upper computer software, the key of the integrated system developed by QT programming tool, can be tracked by laser head and then the damaged part of the PDC bit can be repaired. Finally, a laser-cladding experiment for repairing PDC bit is carried out and the feasibility of the proposed scheme of the integrated automatic system and the effectiveness of the dual-robot system implemented via KUKA robots are verified, According to existing literature, no papers about such integrated system for automatic laser cladding repair have been published. Full article

In recent years, test-based automatic program repair has attracted widespread attention. However, the test suites in practice are not perfect ways to guarantee the correctness of patches generated by repair tools, and weak test suites lead to a large number of incorrect patches produced by the existing repair tool. To reduce the number of incorrect patches generated by repair tools, we propose a patch quality evaluation method based on syntax distance metrics, which measures the syntax distance of patches through four evaluation features—variable, expression, structure, and repair location. By fusing the distance values of the four features, the quality of a patch can be evaluated. Our method evaluates 368 patches from multiple famous repair tools, such as jKali, Nopol, SimFix, DynaMoth, and CapGen; 95% of the correct patches were ranked in the top one of the plausible patches for each defect, which indicates our method can find high-quality patches. Full article

Automatic program repair (APR) is an effective technique for eliminating defects. The repair of null pointer dereferences, as the most common defects, requires accurate dependencies among statements to determine where to repair and how to repair. In order to precisely identify the data and control dependencies, the program dependency graph is adopted. Based on the symmetry among a large number of patches, we propose four repair mechanisms in this passage, namely the assignment mechanism, restraint mechanism, evading mechanism, and transfer mechanism, and employ the decision tree algorithm to match the best repair mechanism for defects. The four repair mechanisms locate the accurate repair position using the program dependency graph, and generate candidate patches by reassigning the null pointer with an appropriate value, making a judgment for the null value in advance, or throwing an exception. Our method was implemented in the repair tool DTSFix, which supports the automatic repair of null pointer dereference in Java programs. The experimental result on Defects4J shows that 73% of null pointer dereferences are successfully repaired by DTSFix, and that the generated candidate patches do not contain over-fitting patches. Full article

It is well known that in a turbulent world (full of processed information), information has a particularly high value. Wide access to technology makes the data transformation and transmission processes faster and more vulnerable to leaks and unauthorized access. This publication discusses the concept of information security management based on a development of a machine called documentomat. It is the machine built to automatically release documents with regard to high security standards. The entire flow of the documents in the company has been presented and the formalized process of decision-making to develop and finally build above-mentioned machine was described. The problems and doubts raised during the machine building process were presented along with the solutions adopted. The device has been designed, constructed, and programmed within the resources of one micro-sized limited liability company in Europe. Thus, it is the solution working in a real world that may find application in many similar companies, not the theoretical concept only. The final result exceeded the expectations of the project team, leading to strengthening of the competitive position of the company. This occurred through the following aspects: adding significant advantage to its offer, which is the ability to release the documents at any time with regard to high security standards. Moreover, the long-term level of costs of such a solution seems to be very low. During the one year of operation period, two malfunctions took place, each was repaired in just 3 h (repair and improvement, labor only, no spare parts needed). Any further maintenance, repair, or development can be done at very affordable prices, at non-available on the market labor cost, which will potentially further strengthen the impact of the solution on the competitiveness of the company. Improvement in terms of competitiveness of the company and data safety has been gained. The success of this project proves that, in some cases, it is worth to develop the necessary solution within the resources of the company rather than buying one from an external enterprise. 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

Recently, software, especially CPS and Internet of Things (IoT), increasingly have high requirements for quality, while program defects exist inevitably duo to the high complexity. Program defect repair faces serious challenges in that such repairs require considerable manpower, and the existing automatic repair approaches have difficulty generating correct patches efficiently. This paper proposes an automatic method for repairing semantic defects in Java programs based on restricted sets which refer to the interval domains of related variables that can trigger program semantic defects. Our work introduces a repair mechanism symmetrically combining defect patterns and repair templates. First, the program semantic defects are summarized into defect patterns according to their grammar and semantic features. A repair template for each type of defect pattern is predefined based on a restricted-set. Then, for each specific defect, a patch statement is automatically synthesized according to the repair template, and the detected defect information is reported by the static detection tool (DTSJava). Next, the patch location is determined by the def-use chain of defect-related variables. Finally, we evaluate the patches generated by our method using DTSJava. We implemented the method in the defect automatic repair prototype tool DTSFix to verify the effect of repairing the semantic defects detected by DTSJava in 6 Java open-source projects. The experimental results showed that 109 of 129 program semantic defects were repaired. Full article