Название: Software Engineering and Management: Theory and Applications: Volume 17
Автор: Roger Lee
Издательство: Springer
Серия: Studies in Computational Intelligence, 1193
Год: 2025
Страниц: 262
Язык: английский
Формат: pdf (true)
Размер: 15.2 MB

The main purpose of this book is to seek peer-reviewed original research papers on the foundations and new developments in Software Engineering. The focus will also be on publishing in a timely manner, the results of applying new and emerging technologies originating from research in Software Engineering. Software Engineering should be applied to a variety of areas, and applications can range across many fields.

Extracting Educational Code Examples from Python Textbooks: Python serves as a versatile programming language due to its diverse audience. This versatility, however, has a double-edged nature as it implies a multitude of learning examples, which can quickly become overwhelming for students. To explore these examples in an educational context, we investigate their usage in Python textbooks. Our approach involves the manual curation of 1,017 chapter titles from 76 Python textbooks. The results indicate that coding examples are prevalent, making up approximately 39.5% of the content compared to other topics. We then classified the example content into four types: Application Programming Interfaces, Data and Processing, Graphical User Interfaces, and others. We also identified a list of 19 Python libraries used in these various examples. To help educators and students, we showcase PyEdu, a Python Educational Example Visualizer that uses the results of the empirical study.

A Preliminary Study on Self-contained Libraries in the NPM Ecosystem: The widespread of libraries within modern software ecosystems creates complex networks of dependencies. These dependencies are fragile to breakage, outdated, or redundancy, potentially leading to cascading issues in dependent libraries. One mitigation strategy involves reducing dependencies; libraries with zero dependencies become self-contained. In software development, a software library refers to pre-built, reusable code modules or libraries that developers integrate into their projects to enhance functionality processes. The term dependency in the context of software libraries refers to the reliance of the main library on external modules to function correctly. These dependencies are crucial in ensuring the proper execution of the software by providing essential functionality. Nowadays, the widespread of libraries within modern software ecosystems creates complex networks of dependencies (e.g., NPM for jаvascript, PyPI for Python, and Maven for Java).

Illustration or Illusion? Reassessing the Use of Machine Learning in Phishing Email Detection: Despite previous research illustrated the very high accuracy of Machine Learning (ML) algorithms in detecting phishing emails, billions of people continue to fall victim to email phishing, often due to detection systems failing to catch them. This study reassesses the use of ML algorithms in detecting previously unseen phishing emails and their ability to identify new phishing tactics. We apply three distinct ML algorithms across seven groups of datasets, and the findings of the thorough quantitative analysis indicate that the performance of ML algorithms substantially varies when evaluated on previously unseen or recent emails. We highlight the difficulty in generalizing learned patterns and we recommend potential solutions to address the identified challenges.

Analyzing ChatGPT Assistance in Programming: In this study, we investigate how developers interact with ChatGPT when seeking programming assistance to identify trends in language preferences and access communication efficiency. We analyze a public dataset, DevGPT, comprising 17,622 ChatGPT prompts, and 12,031 code snippets. Our findings show Python, Bash, and jаvascript as primary languages used in developer-ChatGPT interactions. We find that data manipulation/exchange languages/formats, namely JSON and SQL, require fewer interactions, while general-purpose languages such as C++ and C# need lengthier conversations. The findings provide valuable insights for improving training programs and enhancing ChatGPT’s language understanding, ultimately optimizing collaboration between developers and AI tools in software development.

We identify a total of 109 diverse languages. Our observations reveal that developers require help with variety of languages that span different categories, including but not limited to backend languages such as Java, C#, PHP, and Ruby; frontend technologies like jаvascript, HTML, and CSS; systems programming languages such as C and C++; as well as emerging languages like Kotlin, Rust, and Swift. This diversity reflects the varied requirements of developers in different application domains. It is important to note that ChatGPT is capable of assisting this wide spectrum of developer needs.






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