Field Of Software Engineering Research Articles

Survival of software reuse: Node.js packages component selection and dependencies

Software reuse is a widely recognized and extensively studied practice in the field of software engineering. It involves using pre-existing software components to enhance productivity and efficiency. Node.js and its package manager enable software reuse by managing dependencies. Dependencies play a crucial role in defining the connections and interactions between various components of a software system. The selection of components and their interactions can greatly impact the functionality, performance, and stability of a software system. Overlooking these factors or making poor choices regarding component selection may lead to incomplete or flawed systems. Various factors that impact the decision-making process for selecting components were discussed, including documentation availability, crowdsourcing initiatives, and GitHub activities.

МЕТОДИ ОЦІНКИ ВІДПОВІДНОСТІ ПСЕВДОВИПАДКОВОЇ ПОСЛІДОВНОСТІ ДО ВИПАДКОВОГО РОЗПОДІЛУ В ЗАДАЧАХ АВТОМАТИЗАЦІЇ, КІБЕРБЕЗПЕЦІ, КОМП’ЮТЕРНО-ІНТЕГРОВАНИХ ТЕХНОЛОГІЯХ ТА НЕРУЙНІВНОМУ КОНТРОЛІ

Creation of methods for determining the degree of closeness of a given pseudorandom sequence to a random distribution is a very relevant task in many fields where pseudorandom numbers are used. For example, in cryptography it is important that pseudorandom sequences are unpredictable and have the properties of random numbers. Methods for determining proximity to a random distribution help in assessing the quality of such sequences and identifying flaws in them. In scientific research and engineering projects, pseudorandom numbers are used to model complex systems and simulate random phenomena. It is important to have a means of determining how well such numbers reproduce a random process. In statistics, it is important that pseudorandom sequences meet the requirements of various statistical properties. Methods of determining proximity to a random distribution help in evaluating the properties of these sequences. In software engineering and other fields, it is important to test the quality of pseudorandom number generators to avoid vulnerabilities and flaws in programs. Thus, the development and improvement of methods for determining the degree of closeness of pseudorandom sequences to a random distribution is an urgent task with important practical applications.

Novel Approach to Design a Model for Software Effort Estimation Using Linear Regression

Estimation of an effort is necessary to decide the time and cost needed for the completion of the project. Making judgments with a high degree of uncertainty is a serious issue in the field of software engineering. Software quality forecasting calls for highly precise technologies and expert knowledge. The forecast of software effort based on past data from software development metrics could, instead, be aided by AI-based predictive models, which have a high degree of accuracy. Using a linear regression model, we developed a software effort estimation model in this study to forecast this effort. A non-parametric linear regression approach based on K-Nearest Neighbors was used to create this statistical model (KNN). So, with a 76% coefficient of determination, our findings indicate the potential for applying AI algorithms to predict the software engineering work prediction problem.

A Comprehensive Study on Ensemble Methods for Software Error Detection

Software Developers often find it painful and tedious to locate or pinpoint the errors that reside in the source code which causes serious hindrance in the progression of developing any software. In the field of software engineering, it is very crucial to understand the software metrics that are directly involved with the progression of the software. Besides, various classification algorithms have been used to foresee the errors in building the software. In this paper, we focus especially on ensemble algorithms as they tend to provide more precise and statistically efficient outcomes than the other traditional algorithms. This paper contains twenty software metrics that are pivotal in identifying errors in software applications. Eight Java projects have been gathered to showcase the significance of the software metrics in predicting errors. In this study, three ensemble methods are considered, MultiBoostAB, Dagging, and Decorate. For a detailed inspection of the performance, accuracy, recall, precision, F-measure, and ROC Curve were appraised. The comparisons exhibit Decorate as the highest-performing method and Dagging as the lowest.

Construction and Exploration of Experimental Teaching Content in Software Engineering from a Systems Theory Perspective

Software engineering, as a branch of traditional computer science, distinguishes itself from other computer-related disciplines by its emphasis on practical application. Experimental teaching in software engineering, integral to professional education, serves as a crucial means to enhance students’ practical skills. The content of experimental teaching, serving as a vessel for such instruction, plays a pivotal role in the holistic development of students. Addressing the current fragmented state of experimental teaching in applied universities, this study introduces a holistic and systematic view based on systems theory. By integrating the fundamental principles and methods of systems theory with the distinctive characteristics of software engineering, this study focuses on analyzing the holistic technology of software implementation and the interrelationships among various technical elements of software systems within the field of software engineering. From a holistic-part-holistic analytical perspective, this research constructs an analytical model for experimental teaching content in software engineering under the lens of systems theory. The study elaborates on the construction method of this model and its specific implementation details from the perspectives of talent cultivation programs, engineering elements, and concentrated practical exercises. Practical evidence indicates that the implementation of this experimental teaching content effectively enhances students’ practical skills in software engineering, elevates students’ recognition of their field of study, boosts their confidence in job placement, and provides robust support for seamless transition from graduation to employment. Furthermore, this study sheds light on the construction of experimental teaching content in computer-related fields in engineering disciplines at other applied universities.

Optimizing OCR Performance for Programming Videos: The Role of Image Super-Resolution and Large Language Models

The rapid evolution of video programming tutorials as a key educational resource has highlighted the need for effective code extraction methods. These tutorials, varying widely in video quality, present a challenge for accurately transcribing the embedded source code, crucial for learning and software development. This study investigates the impact of video quality on the performance of optical character recognition (OCR) engines and the potential of large language models (LLMs) to enhance code extraction accuracy. Our comprehensive empirical analysis utilizes a rich dataset of programming screencasts, involving manual transcription of source code and the application of both traditional OCR engines, like Tesseract and Google Vision, and advanced LLMs, including GPT-4V and Gemini. We investigate the efficacy of image super-resolution (SR) techniques, namely, enhanced deep super-resolution (EDSR) and multi-scale deep super-resolution (MDSR), in improving the quality of low-resolution video frames. The findings reveal significant improvements in OCR accuracy with the use of SR, particularly at lower resolutions such as 360p. LLMs demonstrate superior performance across all video qualities, indicating their robustness and advanced capabilities in diverse scenarios. This research contributes to the field of software engineering by offering a benchmark for code extraction from video tutorials and demonstrating the substantial impact of SR techniques and LLMs in enhancing the readability and reusability of code from these educational resources.

Actionable code smell identification with fusion learning of metrics and semantics

Code smell detection is one of the essential tasks in the field of software engineering. Identifying whether a code snippet has a code smell is subjective and varies by programming language, developer, and development method. Moreover, developers tend to focus on code smells that have a real impact on development and ignore insignificant ones. However, existing static code analysis tools and code smell detection approaches exhibit a high false positive rate in detecting code smells, which makes insignificant smells drown out those smells that developers value. Therefore, accurately reporting those actionable code smells that developers tend to spend energy on refactoring can prevent developers from getting lost in the sea of smells and improve refactoring efficiency. In this paper, we aim to detect actionable code smells that developers tend to refactor. Specifically, we first collect actionable and non-actionable code smells from projects with numerous historical versions to construct our datasets. Then, we propose a dual-stream model for fusion learning of code metrics and code semantics to detect actionable code smells. On the one hand, code metrics quantify the code's structure and even some rules or patterns, providing fundamental information for detecting code smells. On the other hand, code semantics encompass information about developers' refactoring tendencies, which prove valuable in detecting actionable code smells. Extensive experiments show that our approach can detect actionable code smells more accurately compared to existing approaches.

Construction and Reflection of Software Engineering Major Based on Accreditation Board for Engineering and Technology (ABET) Certification

With the rapid development of information technology, the demand for talents in the field of software engineering is growing. In order to cultivate high-quality software engineering talents who meet the market demand, universities have continuously carried out the construction of software engineering majors. Accreditation Board for Engineering and Technology (ABET) certification, as an internationally recognized higher education quality assurance system, provides important reference and guidance for the construction of software engineering majors. Guided by student learning outcomes and core competencies, combined with the characteristics of software engineering talent cultivation, the innovation of talent cultivation mode takes industry-education integration and school-enterprise cooperation as the main development paths and explores comprehensive reform of the major in terms of professional positioning and goals, curriculum system, teaching conditions, and teachers. This comprehensive reform model has effectively promoted the development of major construction and improved the quality of talent cultivation.

Exploring ChatGPT’s code refactoring capabilities: An empirical study

ChatGPT has shown great potential in the field of software engineering with its ability to generate code. Yet, ChatGPT’s ability to interpret code has been deemed unreliable and faulty, which causes concern for the platform’s ability to properly refactor code. To confront this concern, we carried out a study to assess ChatGPT’s abilities and limitations in refactoring code. We divided the study into three parts: if ChatGPT can refactor the code, if the refactored code preserves the behavior of the original code segments, and if ChatGPT is capable of providing documentation for the refactored code to provide insights into intent, instructions, and impact. We focused our research specifically on eight quality attributes to use when prompting ChatGPT to refactor our dataset of 40 Java code segments. After collecting the refactored code segments from ChatGPT, as well as data on whether the behavior was preserved, we ran the refactored code through PMD, a source code analyzer, to find programming flaws. We also tested ChatGPT’s accuracy in generating documentation for the refactored code and analyzed the difference between the results of each quality attribute. We conclude that ChatGPT can provide many useful refactoring changes that can improve the code quality which is crucial. ChatGPT offered improved versions of the provided code segments 39 out of 40 times even if it is as simple as suggesting clearer names for variables or better formatting. ChatGPT was able to recommend numerous options ranging from minor changes such as renaming methods and variables to major changes such as modifying the data structure. ChatGPT’s strengths and accuracy were in suggesting minor changes because it had difficulty addressing and understanding complex errors and operations. Although most of the changes were minor, they made significant improvements because converting loops, simplifying calculations, and removing redundant statements have a crucial effect on runtime, memory, and readability. However, our results also indicate how ChatGPT can be unpredictable in its responses which threatens the reliability of ChatGPT. Asking ChatGPT the same prompt often yields different results, so some outputs were more accurate than others. This makes it difficult to fully access ChatGPT’s capabilities due to its variation and inconsistency. Due to ChatGPT’s limitations of its reliance on its data set, it lacks understanding of the broader context so it may occasionally make errors and suggest alternations that are neither applicable nor necessary. Overall, ChatGPT has proved to be a beneficial tool for programming as it is capable of providing advantageous suggestions, even if it is on a small scale. However, human programmers are still needed to oversee these changes and determine their significance. ChatGPT should be used as an aid to programmers since we cannot completely depend on it yet.

Towards Agility in Breast Cancer Treatment Principles as Adopted from Agile Software Engineering.

The complex nature of breast cancer demands flexible and adaptable principles that can account for the diverse characteristics and evolving conditions of each patient. However, there are no common breast cancer treatment agility principles that can influence policies and direct breast cancer professionals and healthcare providers into enhancing the delivery of health outcomes to patients under these conditions along with continuous rapid improvements in breast cancer treatment plan design. The incorporation of agile principles from software engineering offers a promising avenue for enhancing patient care. This research is conducted to identify breast cancer treatment agility principles adopted from the software engineering field and to validate their conformance to agility through work reported from literature in breast cancer treatment context. The authors applied a structured research methodology that involved interviews for eliciting and validating twelve agility principles from oncologists. Discussion of each principle is reflected using work reported from literature as a form of validation. Finally, a domain expert reviewed the literature-driven validation for each of the twelve identified breast cancer treatment agile principle to finally validate their conformance to agility and provide results. This work resulted twelve validated agility principles for breast cancer treatment and classified whether they are meeting, partially-(hybrid), or not meeting agility. Seven out of the twelve agile principles resulted as meeting agility, where the remaining five principles resulted as partially meeting agility. None of them is recorded as not meeting agility. The work contributes to forming an agile mindset that can empower breast cancer professionals to optimize treatment plans, enhance patient experiences, and continuously improve the quality of care. The twelve identified agile principles are anticipated to contribute to driving more efficient oncology practices, policies, and protocols. It is concluded that the breast cancer treatment agility principles are not limited to twelve.

Software Effort Estimation using Machine Learning Algorithms

Effort estimation is a crucial aspect of software development, as it helps project managers plan, control, and schedule the development of software systems. This research study compares various machine learning techniques for estimating effort in software development, focusing on the most widely used and recent methods. The paper begins by highlighting the significance of effort estimation and its associated difficulties. It then presents a comprehensive overview of the different categories of effort estimation techniques, including algorithmic, model-based, and expert-based methods. The study concludes by comparing methods for a given software development project. Random Forest Regression algorithm performs well on the given dataset tested along with various Regression algorithms, including Support Vector, Linear, and Decision Tree Regression. Additionally, the research identifies areas for future investigation in software effort estimation, including the requirement for more accurate and reliable methods and the need to address the inherent complexity and uncertainty in software development projects. This paper provides a comprehensive examination of the current state-of-the-art in software effort estimation, serving as a resource for researchers in the field of software engineering.

Survey of MCDM-Based Software Engineering Method

Multi-criteria decision making (MCDM) is an operation research subfield (OR). Decision-making is primarily a process involving various actors, including individuals, groups of individuals, institutions, and the state. As a discipline, multi-criteria decision-making is relatively new Many researchers have devoted their time to developing new MCDM models and techniques since the 1950s and 1960s, when the foundations of modern multi-criteria decision-making methods were laid. Research and development in the area have intensified recently and appear to be developing tremendously. Few attempts have been made to systematically convey the theoretical foundations and advancements of multi-criteria decision-making systems, despite the widespread and intense development. The framework for evaluating judgments and the methodological options are still up for debate. In this paper reviews of MCDM/MADM technologies will be included describing their scenarios. Making a decision that is more appropriate than others to solve a particular problem is the goal of decision-making (DM), so this study presents a review of the advantages and disadvantages of different decision-making techniques, specifically in the field of software engineering.

A Meta-Model Architecture and Elimination Method for Uncertainty Modeling

Uncertainty exists widely in various fields, especially in industrial manufacturing. From traditional manufacturing to intelligent manufacturing, uncertainty always exists in the manufacturing process. With the integration of rapidly developing intelligent technology, the complexity of manufacturing scenarios is increasing, and the postdecision method cannot fully meet the needs of the high reliability of the process. It is necessary to research the pre-elimination of uncertainty to ensure the reliability of process execution. Here, we analyze the sources and characteristics of uncertainty in manufacturing scenarios and propose a meta-model architecture and uncertainty quantification (UQ) framework for uncertainty modeling. On the one hand, our approach involves the creation of a meta-model structure that incorporates various strategies for uncertainty elimination (UE). On the other hand, we develop a comprehensive UQ framework that utilizes quantified metrics and outcomes to bolster the UE process. Finally, a deterministic model is constructed to guide and drive the process execution, which can achieve the purpose of controlling the uncertainty in advance and ensuring the reliability of the process. In addition, two typical manufacturing process scenarios are modeled, and quantitative experiments are conducted on a simulated production line and open-source data sets, respectively, to illustrate the idea and feasibility of the proposed approach. The proposed UE approach, which innovatively combines the domain modeling from the software engineering field and the probability-based UQ method, can be used as a general tool to guide the reliable execution of the process.

A survey of energy concerns for software engineering

There is growing attention to energy efficiency in the software engineering field. This has been driven by modern technologies, for example, Internet of Things (IoT), Social Networking Services (SNS) and quantum computing. In addition to this, recent trends and concerns such as Environment, Social, and Governance (ESG) and human/societal/environmental well-being for responsible Artificial Intelligence (AI) have accelerated the use of energy efficient software. Despite this, energy concerns in this field have been less explored and studied. This limitation results in falling short to address and overcome greenability issues at the software level, and leaving critical challenges to be solved in this space. This study aims to address this limitation and fill the gap between previous studies. We survey green in software engineering framed by the ten knowledge areas of software engineering to not only cover the entire development life-cycle but also widen the scope of discussion to software process, method, and model management. Based on our comprehensive investigation, we discuss open challenges, trade-offs and implications of this study for both researchers and practitioners.

Barriers of Agile Requirements Engineering in the Public Sector: A Systematic Literature Review

Aim/Purpose: The objective of this study is to summarize the challenges of Agile Requirements Engineering (Agile RE) in the public sector in republican and constitutional monarchy nations. Additionally, it offers recommendations to address these challenges. Background: Failure of IT projects in the public sector results in financial losses for the state and loss of public trust, often attributed to issues in requirements engineering such as prioritization of user needs and excessive scope of requirements. IT projects can have a higher success rate with Agile RE, but there are also drawbacks. Therefore, this study holds significance by presenting a thorough framework designed to pinpoint and overcome the challenges associated with Agile RE to increase the success rate of IT projects. Methodology: This study employs a Systematic Literature Review (SLR) protocol in the field of software engineering or related domains, which consists of three main phases: planning the review, conducting the review with a snowballing approach, and reporting the review. Furthermore, the authors perform open coding to categorize challenges based on the Agile methodologies adoption factor model and axial coding to map potential solutions. Contribution: The authors assert that this research enriches the existing literature on Agile RE, specifically within the public sector context, by mapping out challenges and possible solutions that contribute to creating a foundation for future studies to conduct a more in-depth analysis of Agile adoption in the public sector. Furthermore, it compares the barriers of Agile RE in the public sector with the general context, leading to the discovery of new theories specifically for this field. Findings: Most challenges related to Agile RE in the public sector are found in the people and process aspects. Project and organizational-related are subsequent aspects. Therefore, handling people and processes proficiently is imperative within Agile RE to prevent project failure. Recommendations for Practitioners: Our findings offer a comprehensive view of Agile RE in the public sector in republican and constitutional monarchy nations. This study maps the challenges encountered by the public sector and provides potential solutions. The authors encourage practitioners to consider our findings as a foundation for adopting Agile methodology in the public sector. Furthermore, this study can assist practitioners in identifying existing barriers related to Agile RE, pinpointing elements that contribute to overcoming those challenges, and developing strategies based on the specific needs of the organizations. Recommendation for Researchers: Researchers have the potential to expand the scope of this study by conducting research in other countries, especially African countries, as this study has not yet encompassed this geographic region. Additionally, they can strengthen the evidence linking Agile RE challenges to the risk of Agile project failure by performing empirical validation in a specific country. Impact on Society: This research conducts a comprehensive exploration of Agile RE within the public sector, serving as a foundation for the successful adoption of Agile methodology by overcoming obstacles related to Agile RE. This study highlights the importance of managing people, processes, projects, and organizational elements to increase the success of Agile adoption in the public sector. Future Research: In the future, researchers should work towards resolving the limitations identified in this study. This study has not provided a clear prioritization of challenges and solutions according to their significance. Therefore, future researchers can perform a Fuzzy Analytical Hierarchical Process (F-AHP) to prioritize the proposed solutions.

Comparing the Pretrained Models of Source Code by Re-pretraining Under a Unified Setup.

Recent years have seen the successful application of large pretrained models of source code (CodePTMs) to code representation learning, which have taken the field of software engineering (SE) from task-specific solutions to task-agnostic generic models. By the remarkable results, CodePTMs are seen as a promising direction in both academia and industry. While a number of CodePTMs have been proposed, they are often not directly comparable because they differ in experimental setups such as pretraining dataset, model size, evaluation tasks, and datasets. In this article, we first review the experimental setup used in previous work and propose a standardized setup to facilitate fair comparisons among CodePTMs to explore the impacts of their pretraining tasks. Then, under the standardized setup, we re-pretrain CodePTMs using the same model architecture, input modalities, and pretraining tasks, as they declared and fine-tune each model on each evaluation SE task for evaluating. Finally, we present the experimental results and make a comprehensive discussion on the relative strength and weakness of different pretraining tasks with respect to each SE task. We hope our view can inspire and advance the future study of more powerful CodePTMs.

Transcompiler CNN-GMN Based Cross Language Clone Detection and Algorithm Recognition

Abstract: Software replication stemming from code reutilization introduces complexities in software maintenance. While deep learning-driven clone detection tools abound, they predominantly cater to singular programming languages. Concurrently, the task of identifying algorithms within program code remains challenging due to the absence of metadata, rendering the selection process intricate. This research seeks to establish synergies between these disparate domains by advancing innovative methodologies. The study delves into the unexplored domain of harnessing data augmentation, particularly leveraging transcompiler-based techniques, to enhance the discernment of code clones across different programming languages. Drawing upon the insights derived from transcompilers, this investigation applies data augmentation through source-to-source translation, exemplified by the Transcoder. This method extends the applicability of single-language models, such as the Graph Matching Network (GMN), to encompass cross-language detection. Concurrently, a program code classification model is introduced, leveraging Convolutional Neural Networks (CNNs) to discern algorithms based on structural features (SFs). These SFs are extracted from program codes and subsequently transformed into a one-hot binary matrix. The CNN model undergoes meticulous fine-tuning, optimizing its structural configurations and hyperparameters for superior algorithm classification. This research signifies an integration of disparate realms, intertwining cross-language clone detection and algorithm identification within a unified framework. The exploration of data augmentation for cross-language clone detection and the employment of CNN-GMNs for algorithm identification converge to furnish significant contributions to both domains. These insights, thus, hold promise for advancing the fields of software engineering and programming education

Intelligent Visual Representation for Java Code Data in the Field of Software Engineering Based on Remote Sensing Techniques

In the field of software engineering, large and complex code bases may lead to some burden of understanding their structure and meaning for developers. To reduce the burden on developers, we consider a code base visualization method to visually express the meaning of code bases. Inspired by remote sensing imagery, we employ graphical representations to illustrate the semantic connections within Java code bases, aiming to help developers understand its meaning and logic. This approach is segmented into three distinct levels of analysis. First, at the project-level, we visualize Java projects by portraying each file as an element within a code forest, offering a broad overview of the project’s structure. This macro-view perspective aids in swiftly grasping the project’s layout and hierarchy. Second, at the file-level, we concentrate on individual files, using visualization techniques to highlight their unique attributes and complexities. This perspective enables a deeper understanding of each file’s structure and its role within the larger project. Finally, at the component-level, our focus shifts to the detailed analysis of Java methods and classes. We examine these components for complexity and other specific characteristics, providing insights that are crucial for the optimization of code and the enhancement of software quality. By integrating remote sensing technology, our method offers software engineers deeper insights into code quality, significantly enhancing the software development lifecycle and its outcomes.

Enhancing source code retrieval with joint Bi-LSTM-GNN architecture: A comparative study with ChatGPT-LLM

Retrieving relevant source code from large repositories is a significant and ongoing challenge in the field of software engineering, primarily due to the vast and ever-expanding amount of available code. Existing deep learning methods, although effective to some extent, exhibit limitations in capturing the intricate and complex structural information embedded within source code, which hinders their ability to provide highly accurate retrieval results. This study endeavors to tackle this prominent issue by introducing a novel and innovative approach known as the Joint Bi-directional LSTM and Graph Neural Networks (JBLG) model for source code retrieval. The central aim is to harness the combined strengths and capabilities of Bi-directional Long Short-Term Memory (LSTM) networks and Graph Neural Networks (GNNs) to significantly enhance the model’s capacity to capture and interpret the complex structural characteristics intrinsic to source code. The proposed JBLG model employs a unique fusion of Bi-directional LSTM, which excels in capturing sequential and temporal dependencies within code, and GNN, which is adept at modeling the intricate graph structure of the code. By leveraging this hybrid architecture, the model aims to provide a comprehensive and highly effective solution for source code retrieval tasks. To assess the efficacy of the JBLG model, extensive experiments are conducted, and the model’s performance is evaluated against well-established benchmarks, including LSTM, GNN, and ChatGPT, using two diverse datasets: CodeSearchNet and CosBench datasets. These evaluations span multiple programming languages, ensuring a comprehensive and robust assessment of the model’s capabilities. The experimental results indicate that the JBLG model consistently outperforms its counterparts, including Bi-LSTM, GNN, ChatGPT, and DGMS, across various evaluation metrics. the JBLG model showcases an exceptional ability to handle and extract the intricate structural information inherent in source code, resulting in significantly enhanced retrieval accuracy. The JBLG model emerges as a highly promising solution for real-world source code retrieval applications, with the potential to revolutionize the field. The success of this model underscores the importance of combining deep learning techniques like Bi-directional LSTM and GNNs for tackling complex software engineering challenges. Furthermore, future research directions could involve exploring advanced techniques such as attention mechanisms and extending the model’s applicability to other software engineering tasks like code summarization and code completion. The findings of this study are expected to have a lasting impact on the advancement of source code retrieval methodologies.

Application of knowledge graph in software engineering field: A systematic literature review

Application of knowledge graph in software engineering field: A systematic literature review