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34 pages, 5244 KB

Open AccessArticle

Combining Model-Based Systems Engineering and Knowledge-Centric Systems Engineering to Design Reliable Systems in Practice

by Juan Manuel Morote, Jose Luis de la Vara, Giovanni Giachetti, Clara Ayora and Luis Alonso

Appl. Sci. 2026, 16(5), 2179; https://doi.org/10.3390/app16052179 - 24 Feb 2026

Viewed by 594

Abstract

The use and importance of complex software-intensive systems are growing. As they are used in a wider range of situations in which dependability must be ensured, the reliability of the systems and of their components needs to be addressed throughout their lifecycle, including [...] Read more.

The use and importance of complex software-intensive systems are growing. As they are used in a wider range of situations in which dependability must be ensured, the reliability of the systems and of their components needs to be addressed throughout their lifecycle, including at early development stages. In addition, the means used to deal with reliability need to be linked to and integrated into the overall systems engineering practices and processes. Within this context, we present an approach to design reliable systems in practice in the scope of model-based systems engineering (MBSE) and knowledge-centric systems engineering (KCSE), two systems engineering perspectives whose adoption is increasing. While MBSE relies on explicit system models, KCSE places artificial intelligence at its core to capture, formalise, and reason over system knowledge. Both perspectives are combined to model systems and analyse whether their design addresses the expected system reliability properties, leveraging knowledge representation, natural language processing, and inference mechanisms. The approach links the processes and tools of Arcadia/Capella for MBSE and of SES Engineering Studio for KCSE. A joint application process has been defined for system modelling, ontology development, structured textual requirements specification, traceability management, and model quality analysis, all of which are targeted at system reliability. For validation, the approach has been applied on eight systems that cover five different application domains, considering tens of diagrams, of knowledge elements, of reliability properties, and of analysis possibilities. Based on the validation results, we argue that the approach is a feasible means to design reliable systems. The approach is also the first one that effectively combines MBSE with Arcadia/Capella and KCSE with SES to design reliable systems in practice. Full article

(This article belongs to the Special Issue Artificial Intelligence in Software Engineering)

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28 pages, 1177 KB

Open AccessArticle

Context-Aware Code Review Automation: A Retrieval-Augmented Approach

by Büşra İçöz and Göksel Biricik

Appl. Sci. 2026, 16(4), 1875; https://doi.org/10.3390/app16041875 - 13 Feb 2026

Viewed by 1162

Abstract

Manual code review is essential for software quality, but often slows down development cycles due to the high time demands on developers. In this study, we propose an automated solution for Python (version 3.13) projects that generates code review comments by combining Large [...] Read more.

Manual code review is essential for software quality, but often slows down development cycles due to the high time demands on developers. In this study, we propose an automated solution for Python (version 3.13) projects that generates code review comments by combining Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG). To achieve this, we first curated a dataset from GitHub pull requests (PRs) using the GitHub REST Application Programming Interface (API) (version 2022-11-28) and classified comments into semantic categories using a semi-supervised Support Vector Machine (SVM) model. During the review process, our system uses a vector database to retrieve the top-k most relevant historical comments, providing context for a diverse spectrum of open-weights LLMs, including DeepSeek-Coder-33B, Qwen2.5-Coder-32B, Codestral-22B, CodeLlama-13B, Mistral-Instruct-7B, and Phi-3-Mini. We evaluated the system using a multi-step validation that combined standard metrics (BLEU-4, ROUGE-L, cosine similarity) with an LLM-as-a-Judge approach, and verified the results through targeted human review to ensure consistency with expert standards. The findings show that retrieval augmentation improves feedback relevance for larger models, with DeepSeek-Coder’s alignment score increasing by 17.9% at a retrieval depth of k = 3. In contrast, smaller models such as Phi-3-Mini suffered from context collapse, where too much context reduced accuracy. To manage this trade-off, we built a hybrid expert system that routes each task to the most suitable model. Our results indicate that the proposed approach improved performance by 13.2% compared to the zero-shot baseline (k = 0). In addition, our proposed system reduces hallucinations and generates comments that closely align with the standards expected from the experts. Full article

(This article belongs to the Special Issue Artificial Intelligence in Software Engineering)

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13 pages, 1285 KB

Open AccessArticle

A Rule-Based Method for Detecting Discrepancies in Software Project Productivity Analysis

by Parag C. Pendharkar and James A. Rodger

Appl. Sci. 2026, 16(3), 1170; https://doi.org/10.3390/app16031170 - 23 Jan 2026

Viewed by 251

Abstract

This paper examines traditional data envelopment analysis (DEA), cross efficiency (CE), and game efficiency (GE) models for software productivity analysis and ranking. Additionally, for CE models, secondary objectives of aggressive and benevolent formulations are considered. An entropy criterion is used to identify the [...] Read more.

This paper examines traditional data envelopment analysis (DEA), cross efficiency (CE), and game efficiency (GE) models for software productivity analysis and ranking. Additionally, for CE models, secondary objectives of aggressive and benevolent formulations are considered. An entropy criterion is used to identify the best-performing model. Experiments are conducted using the ISBSG dataset. The results show that aggressive CE model formulations have the lowest entropy values and produce unique project rankings. The GE model is computationally intensive and does not provide sufficient benefits to justify the extra effort. A rule-based framework is introduced to identify discrepancies in project rankings across models. This framework helps managers pinpoint truly efficient projects. Full article

(This article belongs to the Special Issue Artificial Intelligence in Software Engineering)

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21 pages, 2226 KB

Open AccessArticle

Research on Hybrid Collaborative Development Model Based on Multi-Dimensional Behavioral Information

by Shuanliang Gao, Wei Liao, Tao Shu, Zhuoning Zhao and Yaqiang Wang

Appl. Sci. 2025, 15(9), 4907; https://doi.org/10.3390/app15094907 - 28 Apr 2025

Viewed by 2001

Abstract

This paper aims to propose a hybrid collaborative development model based on multi-dimensional behavioral information (HCDMB) to deal with systemic problems in modern software engineering, such as the low efficiency of cross-stage collaboration, the fragmentation of the intelligent tool chain, and the imperfect [...] Read more.

This paper aims to propose a hybrid collaborative development model based on multi-dimensional behavioral information (HCDMB) to deal with systemic problems in modern software engineering, such as the low efficiency of cross-stage collaboration, the fragmentation of the intelligent tool chain, and the imperfect human–machine collaboration mechanism. This paper focuses on the stages of requirements analysis, software development, software testing and software operation and maintenance in the process of software development. By integrating the multi-dimensional characteristics of the development behavior track, collaboration interaction record and product application data in the process of project promotion, the mixture of experts (MoE) model is introduced to break through the rigid constraints of the traditional tool chain. Reinforcement learning combined with human feedback is used to optimize the MoE dynamic routing mechanism. At the same time, the few-shot context learning method is used to build different expert models, which further improve the reasoning efficiency and knowledge transfer ability of the system in different scenarios. The HCDMB model proposed in this paper can be viewed as an important breakthrough in the software engineering collaboration paradigm, so as to provide innovative solutions to the many problems faced by dynamic requirements and diverse scenarios based on artificial intelligence technology in the field of software engineering involving different project personnel. Full article

(This article belongs to the Special Issue Artificial Intelligence in Software Engineering)

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21 pages, 1317 KB

Open AccessArticle

Can Large-Language Models Replace Humans in Agile Effort Estimation? Lessons from a Controlled Experiment

by Luka Pavlič, Vasilka Saklamaeva and Tina Beranič

Appl. Sci. 2024, 14(24), 12006; https://doi.org/10.3390/app142412006 - 22 Dec 2024

Cited by 3 | Viewed by 3652

Abstract

Effort estimation is critical in software engineering to assess the resources needed for development tasks and to enable realistic commitments in agile iterations. This study investigates whether generative AI tools, which are transforming various aspects of software development, can improve effort estimation efficiency. [...] Read more.

Effort estimation is critical in software engineering to assess the resources needed for development tasks and to enable realistic commitments in agile iterations. This study investigates whether generative AI tools, which are transforming various aspects of software development, can improve effort estimation efficiency. A controlled experiment was conducted in which development teams upgraded an existing information system, with the experimental group using the generative-AI-based tool GitLab Duo for estimation and the control group using conventional methods (e.g., planning poker or analogy-based planning). Results show that while generative-AI-based estimation tools achieved only 16% accuracy—currently insufficient for industry standards—they offered valuable support for task breakdown and iteration planning. Participants noted that a combination of conventional methods and AI-based tools could offer enhanced accuracy and efficiency in future planning. Full article

(This article belongs to the Special Issue Artificial Intelligence in Software Engineering)

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29 pages, 1792 KB

Open AccessArticle

AbstractTrace: The Use of Execution Traces to Cluster, Classify, Prioritize, and Optimize a Bloated Test Suite

by Ziad A. Al-Sharif and Clinton L. Jeffery

Appl. Sci. 2024, 14(23), 11168; https://doi.org/10.3390/app142311168 - 29 Nov 2024

Cited by 2 | Viewed by 1605

Abstract

Due to the incremental and iterative nature of the software testing process, a test suite may become bloated with redundant, overlapping, and similar test cases. This paper aims to optimize a bloated test suite by employing an execution trace that encodes runtime events [...] Read more.

Due to the incremental and iterative nature of the software testing process, a test suite may become bloated with redundant, overlapping, and similar test cases. This paper aims to optimize a bloated test suite by employing an execution trace that encodes runtime events into a sequence of characters forming a string. A dataset of strings, each of which represents the code coverage and execution behavior of a test case, is analyzed to identify similarities between test cases. This facilitates the de-bloating process by providing a formal mechanism to identify, remove, and reduce extra test cases without compromising software quality. This form of analysis allows for the clustering and classification of test cases based on their code coverage and similarity score. This paper explores three levels of execution traces and evaluates different techniques to measure their similarities. Test cases with the same code coverage should generate the exact string representation of runtime events. Various string similarity metrics are assessed to find the similarity score, which is used to classify, detect, and rank test cases accordingly. Additionally, this paper demonstrates the validity of the approach with two case studies. The first shows how to classify the execution behavior of various test cases, which can provide insight into each test case’s internal behavior. The second shows how to identify similar test cases based on their code coverage. Full article

(This article belongs to the Special Issue Artificial Intelligence in Software Engineering)

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Review

Jump to: Research, Other

59 pages, 4087 KB

Open AccessReview

Neural Methods for Programming: A Comprehensive Survey and Future Directions

by Gebremedhin Gebreslassie Maru, Sanghwa Lee, Suhwan Ji, Sang-Ki Ko and Hyeonseung Im

Appl. Sci. 2025, 15(22), 12150; https://doi.org/10.3390/app152212150 - 16 Nov 2025

Cited by 2 | Viewed by 3845

Abstract

The advancement of neural-based models has driven significant progress in modern code intelligence, accelerating the development of intelligent programming tools such as code assistants and automated software engineering systems. This study presents a comprehensive and systematic survey of neural methods for programming tasks [...] Read more.

The advancement of neural-based models has driven significant progress in modern code intelligence, accelerating the development of intelligent programming tools such as code assistants and automated software engineering systems. This study presents a comprehensive and systematic survey of neural methods for programming tasks within the broader context of software development. Guided by six research questions, this study synthesizes insights from more than 250 scientific papers, the majority of which were published between 2015 and 2025, with earlier foundational works (dating back to the late 1990s) included for historical context. The analysis spans 18 major programming tasks, including code generation, code translation, code clone detection, code classification, and vulnerability detection. The survey methodologically examines the development and evolution of neural approaches, the datasets employed, and the performance evaluation metrics adopted in this field. It traces the progress in neural techniques from early code modeling approaches to advanced Code-specific Large Language Models (Code LLMs), emphasizing their advantages over traditional rule-based and statistical methods. A taxonomy of evaluation metrics and a categorized summary of datasets and benchmarks reveal both progress and persistent limitations in data coverage and evaluation practices. The review further distinguishes neural models designed for natural language processing and programming languages, highlighting the structural and functional characteristics that influence model performance. Finally, the study discusses emerging trends, unresolved challenges, and potential research directions, underscoring the transformative role of neural-based architectures, particularly Code LLMs, in enhancing programming and software design activities and shaping the future of AI-driven software development. Full article

(This article belongs to the Special Issue Artificial Intelligence in Software Engineering)

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Other

Jump to: Research, Review

37 pages, 4751 KB

Open AccessSystematic Review

Machine Learning-Based Methods for Code Smell Detection: A Survey

by Pravin Singh Yadav, Rajwant Singh Rao, Alok Mishra and Manjari Gupta

Appl. Sci. 2024, 14(14), 6149; https://doi.org/10.3390/app14146149 - 15 Jul 2024

Cited by 22 | Viewed by 8286

Abstract

Code smells are early warning signs of potential issues in software quality. Various techniques are used in code smell detection, including the Bayesian approach, rule-based automatic antipattern detection, antipattern identification utilizing B-splines, Support Vector Machine direct, SMURF (Support Vector Machines for design smell [...] Read more.

Code smells are early warning signs of potential issues in software quality. Various techniques are used in code smell detection, including the Bayesian approach, rule-based automatic antipattern detection, antipattern identification utilizing B-splines, Support Vector Machine direct, SMURF (Support Vector Machines for design smell detection using relevant feedback), and immune-based detection strategy. Machine learning (ML) has taken a great stride in this area. This study includes relevant studies applying ML algorithms from 2005 to 2024 in a comprehensive manner for the survey to provide insight regarding code smell, ML algorithms frequently applied, and software metrics. Forty-two pertinent studies allow us to assess the efficacy of ML algorithms on selected datasets. After evaluating various studies based on open-source and project datasets, this study evaluated additional threats and obstacles to code smell detection, such as the lack of standardized code smell definitions, the difficulty of feature selection, and the challenges of handling large-scale datasets. The current studies only considered a few factors in identifying code smells, while in this study, several potential contributing factors to code smells are included. Several ML algorithms are examined, and various approaches, datasets, dataset languages, and software metrics are presented. This study provides the potential of ML algorithms to produce better results and fills a gap in the body of knowledge by providing class-wise distributions of the ML algorithms. Support Vector Machine, J48, Naive Bayes, and Random Forest models are the most common for detecting code smells. Researchers can find this study helpful in better anticipating and taking care of software development design and implementation issues. The findings from this study, which highlight the practical implications of ML algorithms in software quality improvement, will help software engineers fix problems during software design and development to ensure software quality. Full article

(This article belongs to the Special Issue Artificial Intelligence in Software Engineering)

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