Transformer-based models application for bug detection in source code

Illia Vokhranov, Bogdan Bulakh — Sat, 31 Aug 2024 00:00:00 +0300

This paper explores the use of transformer-based models for bug detection in source code, aiming to better understand the capacity of these models to learn complex patterns and relationships within the code. Traditional static analysis tools are highly limited in their ability to detect semantic errors, resulting in numerous defects passing through to the code execution stage. This research represents a step towards enhancing static code analysis using neural networks.

The experiments were designed as binary classification tasks to detect buggy code snippets, each targeting a specific defect type such as NameError, TypeError, IndexError, AttributeError, ValueError, EOFError, SyntaxError, and ModuleNotFoundError. Utilizing the «RunBugRun» dataset, which relies on code execution results, the models – BERT, CodeBERT, GPT-2, and CodeT5 – were fine-tuned and compared under identical conditions and hyperparameters. Performance was evaluated using F1-Score, Precision, and Recall.

The results indicated that transformer-based models, especially CodeT5 and CodeBERT, were effective in identifying various defects, demonstrating their ability to learn complex code patterns. However, performance varied by defect type, with some defects like IndexError and TypeError being more challenging to detect. The outcomes underscore the importance of high-quality, diverse training data and highlight the potential of transformer-based models to achieve more accurate early defect detection.

Future research should further explore advanced transformer architectures for detecting complicated defects, and investigate the integration of additional contextual information to the detection process. This study highlights the potential of modern machine learning architectures to advance software engineering practices, leading to more efficient and reliable software development.

Assessing the impact of multichannel sales integration on the efficiency and competitiveness of Ukrainian retail in the context of digital commerce

Anton Zhuk, Ihor Usoltsev — Sat, 31 Aug 2024 00:00:00 +0300

One of the most significant changes the business world is currently experiencing is the progressive development and adoption of digital commerce. Taking into account the rapid development of web and Internet technologies, e-commerce is increasing volumes on a global scale and is being formed as a separate branch of the economy. Every year from 30 % to 70 % businesses of all countries (regardless of their level of development) are moving to the online environment. This is especially true for business entities that carry out trading activities.

The object of the study is the process of integrating multichannel sales in retail trade in Ukraine, with a focus on its impact on the efficiency and competitiveness of enterprises in the market. The problem under consideration is to determine the most effective methods and strategies for implementing e-commerce in the Ukrainian economy, which is under the influence of martial law and other socio-economic factors.

The main results of the study show that the integration of online and offline sales channels significantly increases business productivity. It was found that the use of omnichannel platforms can significantly improve customer interaction and increase sales. In particular, the analysis showed that properly integrated sales channels can increase the efficiency of enterprises by 20–30 %.

These results can be explained by the high level of adaptability of Ukrainian companies to new technologies and their ability to quickly integrate digital platforms into their business processes. The study also confirmed that businesses that actively use omnichannel strategies achieve higher levels of efficiency and competitiveness.

In practice, these results can be applied to the real-life conditions of Ukrainian retailers. This is especially true for small and medium-sized enterprises seeking to increase their market presence through the introduction of modern technologies and the integration of various sales channels. Using the data obtained will allow companies to optimise their business models, develop effective marketing campaigns and improve customer interaction. The findings can also be applied to the analysis of omnichannel retailing and digital commerce in other countries at different stages of retail development. This study also provides practical recommendations that may be useful for international companies and academics interested in improving retail performance.

Technology audit and production reserves

Transformer-based models application for bug detection in source code

Assessing the impact of multichannel sales integration on the efficiency and competitiveness of Ukrainian retail in the context of digital commerce