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Software, Volume 4, Issue 3 (September 2025) – 5 articles

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48 pages, 2275 KiB  
Article
Intersectional Software Engineering as a Field
by Alicia Julia Wilson Takaoka, Claudia Maria Cutrupi and Letizia Jaccheri
Software 2025, 4(3), 18; https://doi.org/10.3390/software4030018 - 30 Jul 2025
Viewed by 114
Abstract
Intersectionality is a concept used to explain the power dynamics and inequalities that some groups experience owing to the interconnection of social differences such as in gender, sexual identity, poverty status, race, geographic location, disability, and education. The relation between software engineering, feminism, [...] Read more.
Intersectionality is a concept used to explain the power dynamics and inequalities that some groups experience owing to the interconnection of social differences such as in gender, sexual identity, poverty status, race, geographic location, disability, and education. The relation between software engineering, feminism, and intersectionality has been addressed by some studies thus far, but it has never been codified before. In this paper, we employ the commonly used ABC Framework for empirical software engineering to show the contributions of intersectional software engineering (ISE) as a field of software engineering. In addition, we highlight the power dynamic, unique to ISE studies, and define gender-forward intersectionality as a way to use gender as a starting point to identify and examine inequalities and discrimination. We show that ISE is a field of study in software engineering that uses gender-forward intersectionality to produce knowledge about power dynamics in software engineering in its specific domains and environments. Employing empirical software engineering research strategies, we explain the importance of recognizing and evaluating ISE through four dimensions of dynamics, which are people, processes, products, and policies. Beginning with a set of 10 seminal papers that enable us to define the initial concepts and the query for the systematic mapping study, we conduct a systematic mapping study leads to a dataset of 140 primary papers, of which 15 are chosen as example papers. We apply the principles of ISE to these example papers to show how the field functions. Finally, we conclude the paper by advocating the recognition of ISE as a specialized field of study in software engineering. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Software)
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16 pages, 396 KiB  
Article
Investigating Reproducibility Challenges in LLM Bugfixing on the HumanEvalFix Benchmark
by Balázs Szalontai, Balázs Márton, Balázs Pintér and Tibor Gregorics
Software 2025, 4(3), 17; https://doi.org/10.3390/software4030017 - 14 Jul 2025
Viewed by 438
Abstract
Benchmark results for large language models often show inconsistencies across different studies. This paper investigates the challenges of reproducing these results in automatic bugfixing using LLMs, on the HumanEvalFix benchmark. To determine the cause of the differing results in the literature, we attempted [...] Read more.
Benchmark results for large language models often show inconsistencies across different studies. This paper investigates the challenges of reproducing these results in automatic bugfixing using LLMs, on the HumanEvalFix benchmark. To determine the cause of the differing results in the literature, we attempted to reproduce a subset of them by evaluating 12 models in the DeepSeekCoder, CodeGemma, CodeLlama, and WizardCoder model families, in different sizes and tunings. A total of 35 unique results were reported for these models across studies, of which we successfully reproduced 12. We identified several relevant factors that influenced the results. The base models can be confused with their instruction-tuned variants, making their results better than expected. Incorrect prompt templates or generation length can decrease benchmark performance, as well as using 4-bit quantization. Using sampling instead of greedy decoding can increase the variance, especially with higher temperature values. We found that precision and 8-bit quantization have less influence on benchmark results. Full article
(This article belongs to the Topic Applications of NLP, AI, and ML in Software Engineering)
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2 pages, 131 KiB  
Editorial
New Editor-in-Chief of Software
by Mirko Viroli
Software 2025, 4(3), 16; https://doi.org/10.3390/software4030016 - 10 Jul 2025
Viewed by 153
Abstract
I would like to introduce myself as the new Editor-in-Chief of Software [...] Full article
24 pages, 498 KiB  
Article
Analysing Concurrent Queues Using CSP: Examining Java’s ConcurrentLinkedQueue
by Kevin Chalmers and Jan Bækgaard Pedersen
Software 2025, 4(3), 15; https://doi.org/10.3390/software4030015 - 7 Jul 2025
Viewed by 178
Abstract
In this paper we examine the OpenJDK library implementation of the ConcurrentLinkedQueue. We use model checking to verify that it behaves according to the algorithm it is based on: Michael and Scott’s fast and practical non-blocking concurrent queue algorithm. In addition, we [...] Read more.
In this paper we examine the OpenJDK library implementation of the ConcurrentLinkedQueue. We use model checking to verify that it behaves according to the algorithm it is based on: Michael and Scott’s fast and practical non-blocking concurrent queue algorithm. In addition, we develop a simple concurrent queue specification in CSP and verify that Michael and Scott’s algorithm satisfies it. We conclude that both the algorithm and the implementation are correct and both conform to our simpler concurrent queue specification, which we can use in place of either implementation in future verification tasks. The complete code is available on GitHub. Full article
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56 pages, 1008 KiB  
Review
Machine Learning Techniques for Requirements Engineering: A Comprehensive Literature Review
by António Miguel Rosado da Cruz and Estrela Ferreira Cruz
Software 2025, 4(3), 14; https://doi.org/10.3390/software4030014 - 28 Jun 2025
Viewed by 703
Abstract
Software requirements engineering is one of the most critical and time-consuming phases of the software-development process. The lack of communication with stakeholders and the use of natural language for communicating leads to misunderstanding and misidentification of requirements or the creation of ambiguous requirements, [...] Read more.
Software requirements engineering is one of the most critical and time-consuming phases of the software-development process. The lack of communication with stakeholders and the use of natural language for communicating leads to misunderstanding and misidentification of requirements or the creation of ambiguous requirements, which can jeopardize all subsequent steps in the software-development process and can compromise the quality of the final software product. Natural Language Processing (NLP) is an old area of research; however, it is currently undergoing strong and very positive impacts with recent advances in the area of Machine Learning (ML), namely with the emergence of Deep Learning and, more recently, with the so-called transformer models such as BERT and GPT. Software requirements engineering is also being strongly affected by the entire evolution of ML and other areas of Artificial Intelligence (AI). In this article we conduct a systematic review on how AI, ML and NLP are being used in the various stages of requirements engineering, including requirements elicitation, specification, classification, prioritization, requirements management, requirements traceability, etc. Furthermore, we identify which algorithms are most used in each of these stages, uncover challenges and open problems and suggest future research directions. Full article
(This article belongs to the Topic Applications of NLP, AI, and ML in Software Engineering)
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