Program Repair with Minimal Edits Using CodeT5

Programmers often struggle to identify and fix bugs in their programs. In recent years, many language models (LMs) have been proposed to fix erroneous programs and support error recovery. However, the LMs tend to generate solutions that differ from the original input programs. This leads to potential comprehension difficulties for users. In this paper, we propose an approach to suggest a correct program with minimal repair edits using CodeT5. We fine-tune a pre-trained CodeT5 on code pairs of wrong and correct programs and evaluate its performance with several baseline models. The experimental results show that the fine-tuned CodeT5 achieves a pass@100 of 91.95% and an average edit distance of the most similar correct program of 6.84, which indicates that at least one correct program can be suggested by generating 100 candidate programs. We demonstrate the effectiveness of LMs in suggesting program repair with minimal edits for solving introductory programming problems.Comment: 7 pages, 6 figures, accepted to iCAST 202

Exploring Automated Code Evaluation Systems and Resources for Code Analysis: A Comprehensive Survey

The automated code evaluation system (AES) is mainly designed to reliably assess user-submitted code. Due to their extensive range of applications and the accumulation of valuable resources, AESs are becoming increasingly popular. Research on the application of AES and their real-world resource exploration for diverse coding tasks is still lacking. In this study, we conducted a comprehensive survey on AESs and their resources. This survey explores the application areas of AESs, available resources, and resource utilization for coding tasks. AESs are categorized into programming contests, programming learning and education, recruitment, online compilers, and additional modules, depending on their application. We explore the available datasets and other resources of these systems for research, analysis, and coding tasks. Moreover, we provide an overview of machine learning-driven coding tasks, such as bug detection, code review, comprehension, refactoring, search, representation, and repair. These tasks are performed using real-life datasets. In addition, we briefly discuss the Aizu Online Judge platform as a real example of an AES from the perspectives of system design (hardware and software), operation (competition and education), and research. This is due to the scalability of the AOJ platform (programming education, competitions, and practice), open internal features (hardware and software), attention from the research community, open source data (e.g., solution codes and submission documents), and transparency. We also analyze the overall performance of this system and the perceived challenges over the years

Compared the effect of indirect ELISA and serum plate agglutination (SPA) test for the detection of Mycoplasma gallisepticum in chicken

Mycoplasma gallisepticum (MG) is a highly economical and persistent threat of poultry industry in Bangladesh. Indirect ELISA (iELISA) and Serum plate agglutination test (SPA) is available serological test for diagnosis of MG antibodies. The aim of this research was conducted on the basis of comparison on diagnosis results between iELISA and SPA test for MG antibody in same sample in layer chicken. Total 563 serum samples were collected and tested for MG antibody by both iELISA and SPA test. Out of 563 samples 363 (64.48%) samples were positive by iELISA and 316 (56.13%) samples were positive in SPA test. The higher incidence of MG antibody was found in chicken at 50-56 weeks and flock size was 3000-4200 as 69.63% by iELISA and 61.21% by SPA and in Sonali breeds 69.08% by iELISA and 60.64% by SPA. The results showed the comparatively higher number of positive results in iELISA test than SPA test. So the findings of the study demonstrated that a significant (p<0.05) difference between iELISA and SPA test present. The study may helpful for screening the flock for MG and small-holding farmers may use SPA test rather than iELISA test due to rapid, easy and cost effective

ChatGPT for Education and Research: Opportunities, Threats, and Strategies

In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education and research. One such technology is ChatGPT, a powerful large language model developed by OpenAI. This technology offers exciting opportunities for students and educators, including personalized feedback, increased accessibility, interactive conversations, lesson preparation, evaluation, and new ways to teach complex concepts. However, ChatGPT poses different threats to the traditional education and research system, including the possibility of cheating on online exams, human-like text generation, diminished critical thinking skills, and difficulties in evaluating information generated by ChatGPT. This study explores the potential opportunities and threats that ChatGPT poses to overall education from the perspective of students and educators. Furthermore, for programming learning, we explore how ChatGPT helps students improve their programming skills. To demonstrate this, we conducted different coding-related experiments with ChatGPT, including code generation from problem descriptions, pseudocode generation of algorithms from texts, and code correction. The generated codes are validated with an online judge system to evaluate their accuracy. In addition, we conducted several surveys with students and teachers to find out how ChatGPT supports programming learning and teaching. Finally, we present the survey results and analysis

A Neural Network Based Intelligent Support Model for Program Code Completion

In recent years, millions of source codes are generated in different languages on a daily basis all over the world. A deep neural network-based intelligent support model for source code completion would be a great advantage in software engineering and programming education fields. Vast numbers of syntax, logical, and other critical errors that cannot be detected by normal compilers continue to exist in source codes, and the development of an intelligent evaluation methodology that does not rely on manual compilation has become essential. Even experienced programmers often find it necessary to analyze an entire program in order to find a single error and are thus being forced to waste valuable time debugging their source codes. With this point in mind, we proposed an intelligent model that is based on long short-term memory (LSTM) and combined it with an attention mechanism for source code completion. Thus, the proposed model can detect source code errors with locations and then predict the correct words. In addition, the proposed model can classify the source codes as to whether they are erroneous or not. We trained our proposed model using the source code and then evaluated the performance. All of the data used in our experiments were extracted from Aizu Online Judge (AOJ) system. The experimental results obtained show that the accuracy in terms of error detection and prediction of our proposed model approximately is 62% and source code classification accuracy is approximately 96% which outperformed a standard LSTM and other state-of-the-art models. Moreover, in comparison to state-of-the-art models, our proposed model achieved an interesting level of success in terms of error detection, prediction, and classification when applied to long source code sequences. Overall, these experimental results indicate the usefulness of our proposed model in software engineering and programming education arena

A Bidirectional LSTM Language Model for Code Evaluation and Repair

Programming is a vital skill in computer science and engineering-related disciplines. However, developing source code is an error-prone task. Logical errors in code are particularly hard to identify for both students and professionals, and a single error is unexpected to end-users. At present, conventional compilers have difficulty identifying many of the errors (especially logical errors) that can occur in code. To mitigate this problem, we propose a language model for evaluating source codes using a bidirectional long short-term memory (BiLSTM) neural network. We trained the BiLSTM model with a large number of source codes with tuning various hyperparameters. We then used the model to evaluate incorrect code and assessed the model’s performance in three principal areas: source code error detection, suggestions for incorrect code repair, and erroneous code classification. Experimental results showed that the proposed BiLSTM model achieved 50.88% correctness in identifying errors and providing suggestions. Moreover, the model achieved an F-score of approximately 97%, outperforming other state-of-the-art models (recurrent neural networks (RNNs) and long short-term memory (LSTM))

Source Code Assessment and Classification Based on Estimated Error Probability Using Attentive LSTM Language Model and Its Application in Programming Education

The rate of software development has increased dramatically. Conventional compilers cannot assess and detect all source code errors. Software may thus contain errors, negatively affecting end-users. It is also difficult to assess and detect source code logic errors using traditional compilers, resulting in software that contains errors. A method that utilizes artificial intelligence for assessing and detecting errors and classifying source code as correct (error-free) or incorrect is thus required. Here, we propose a sequential language model that uses an attention-mechanism-based long short-term memory (LSTM) neural network to assess and classify source code based on the estimated error probability. The attentive mechanism enhances the accuracy of the proposed language model for error assessment and classification. We trained the proposed model using correct source code and then evaluated its performance. The experimental results show that the proposed model has logic and syntax error detection accuracies of 92.2% and 94.8%, respectively, outperforming state-of-the-art models. We also applied the proposed model to the classification of source code with logic and syntax errors. The average precision, recall, and F-measure values for such classification are much better than those of benchmark models. To strengthen the proposed model, we combined the attention mechanism with LSTM to enhance the results of error assessment and detection as well as source code classification. Finally, our proposed model can be effective in programming education and software engineering by improving code writing, debugging, error-correction, and reasoning

Seroprevalence of Mycoplasma gallisepticum antibody by ELISA and serum plate agglutination test of laying chicken

Aim: Mycoplasma gallisepticum (MG) is important avian pathogen responsible for chronic respiratory disease of chicken and turkeys, which result in large economic loss for the poultry industry. The objectives of this study were determination of seroprevalence of MG antibody of commercial layer chicken at laying period in selected areas of Bangladesh. Materials and Methods: A total of 563 blood samples were collected randomly from selected commercial layer chickens at laying period during the period from July to December, 2013. Indirect enzyme linked immunosorbent assay (iELISA) and serum plate agglutination (SPA) test were performed to detect the presence of antibodies against MG. Results: Of 563 samples, 64.47% and 56.13% showed an overall prevalence of MG antibodies in iELISA and SPA test respectively. Prevalence of MG was recorded the highest (69.63%) at 50-55 weeks of age compared with lowest (53.26%) at 56-61 weeks of age (p<0.05). Significant (p<0.05) effect of breed were observed in the seroprevalence of MG infection in layer birds in the present study. The overall, 68.77%, 63.74% and 59.37% prevalence were found respectively in sonali, ISA Brown and White leg horn. The prevalence of MG antibodies was the highest (70.13%) in December followed by November (68%), October (65.67%), August (63.46%), September (58.54%) and July (51.78%) month. The seroprevalence of MG antibodies was higher (69.63%) in most of the large flocks and lower (56.82%) in small flocks. Conclusion: Therefore, might be suggested that the commercial layer farms should be routinely checked to monitor MG infection and the reactor birds should be culled since MG organism has the potential to transmit vertically. The correlation between MG antibody in month and flock size was not significant (p=0.359 and p=0.868, respectively)

First Evidence of Fowl Adenovirus Induced Inclusion Body Hepatitis in Chicken in Bangladesh

Background. The livestock sector contributes 1.90% to the GDP in Bangladesh during 2021–22. Poultry is one of the important subsectors struggling with diseases. Fowl adenoviruses (FAdVs) cause numerous diseases resulting in economic losses to the poultry industry worldwide. Several FAdV serotypes cause inclusion body hepatitis in chicken. Although FAdV infection was suspected, there was no confirmatory report from Bangladesh. The study was conducted to investigate the FAdV infection and antibodies in chicken. Methods. A total of 50 samples, each composed of liver and spleen, were collected from different chickens of Gazipur, Dinajpur, and Panchagarh district. Each location belongs to A, B, and C poultry zones of Bangladesh, respectively. Viruses were detected by real-time PCR and conventional PCR. Blood samples (n = 303) were collected at the beginning and after the recovery from infection and tested by indirect ELISA. Sequencing of PCR products was done for serotyping and phylogenetic analysis. Results. Clinical signs were observed including anorexia, drowsiness, ruffled feathers, reduced body weight, lack of uniformity, and high mortality (15–25%). Enlarged friable liver with yellow to tan color mottled with the focal soft area, fluid in pericardial sac, swollen and hemorrhagic kidneys, enlarged congested spleen and pancreas, etc. were found on postmortem examination. FAdVs were detected in 90% of the flocks except commercial layer flock from Dinajpur. Three serotypes, namely, 8b (70%), 11 (10%), and 5 (10%) were detected. Anti-FAdV antibody was detected in 80% flocks at the beginning of infection and in 90% of the flocks after recovery from infection. The antibody titer increases significantly (p<0.05) after recovery from infection. Phylogenetic analysis revealed that the Bangladeshi FAdVs have close identity with viruses from Asia, Europe, and South and North America. Conclusions. These findings suggested that several introductions of FAdVs were taken place in Bangladesh. To combat the disease, vaccination along with maintenance of biosecurity is essential