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Software, Volume 5, Issue 1 (March 2026) – 5 articles

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28 pages, 684 KB  
Article
Data-Centric Serverless Computing with LambdaStore
by Kai Mast, Suyan Qu, Aditya Jain, Andrea Arpaci-Dusseau and Remzi Arpaci-Dusseau
Software 2026, 5(1), 5; https://doi.org/10.3390/software5010005 - 21 Jan 2026
Viewed by 146
Abstract
LambdaStore is a data-centric serverless platform that breaks the split between stateless functions and external storage in classic cloud computing platforms. By scheduling serverless invocations near data instead of pulling data to compute, LambdaStore substantially reduces the state access cost that dominates today’s [...] Read more.
LambdaStore is a data-centric serverless platform that breaks the split between stateless functions and external storage in classic cloud computing platforms. By scheduling serverless invocations near data instead of pulling data to compute, LambdaStore substantially reduces the state access cost that dominates today’s serverless workloads. Leveraging its transactional storage engine, LambdaStore delivers serializable guarantees and exactly-once semantics across chains of lambda invocations—a capability missing in current Function-as-a-Service offerings. We make three key contributions: (1) an object-oriented programming model that ties function invocations with its data; (2) a transaction layer with adaptive lock granularity and an optimistic concurrency control protocol designed for serverless workloads to keep contention low while preserving serializability; and (3) an elastic storage system that preserves the elasticity of the serverless paradigm while lambda functions run close to their data. Under read-heavy workloads, LambdaStore lifts throughput by orders of magnitude over existing serverless platforms while holding end-to-end latency below 20 ms. Full article
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19 pages, 495 KB  
Article
Mitigating Prompt Dependency in Large Language Models: A Retrieval-Augmented Framework for Intelligent Code Assistance
by Saja Abufarha, Ahmed Al Marouf, Jon George Rokne and Reda Alhajj
Software 2026, 5(1), 4; https://doi.org/10.3390/software5010004 - 21 Jan 2026
Viewed by 197
Abstract
Background: The implementation of Large Language Models (LLMs) in software engineering has provided new and improved approaches to code synthesis, testing, and refactoring. However, even with these new approaches, the practical efficacy of LLMs is restricted due to their reliance on user-given [...] Read more.
Background: The implementation of Large Language Models (LLMs) in software engineering has provided new and improved approaches to code synthesis, testing, and refactoring. However, even with these new approaches, the practical efficacy of LLMs is restricted due to their reliance on user-given prompts. The problem is that these prompts can vary a lot in quality and specificity, which results in inconsistent or suboptimal results for the LLM application. Methods: This research therefore aims to alleviate these issues by developing an LLM-based code assistance prototype with a framework based on Retrieval-Augmented Generation (RAG) that automates the prompt-generation process and improves the outputs of LLMs using contextually relevant external knowledge. Results: The tool aims to reduce dependence on the manual preparation of prompts and enhance accessibility and usability for developers of all experience levels. The tool achieved a Code Correctness Score (CCS) of 162.0 and an Average Code Correctness (ACC) score of 98.8% in the refactoring task. These results can be compared to those of the generated tests, which scored CCS 139.0 and ACC 85.3%, respectively. Conclusions: This research contributes to the growing list of Artificial Intelligence (AI)-powered development tools and offers new opportunities for boosting the productivity of developers. Full article
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1 pages, 126 KB  
Retraction
RETRACTED: Stephenson, M.J. A Differential Datalog Interpreter. Software 2023, 2, 427–446
by Matthew James Stephenson
Software 2026, 5(1), 3; https://doi.org/10.3390/software5010003 - 21 Jan 2026
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Abstract
The Journal retracts the article titled, “A Differential Datalog Interpreter” [...] Full article
27 pages, 520 KB  
Article
rUnit—A Framework for Test Analysis of C Programs
by Peter Backeman
Software 2026, 5(1), 2; https://doi.org/10.3390/software5010002 - 2 Jan 2026
Viewed by 282
Abstract
Asserting program correctness is a longstanding challenge in software development that consumes lots of resources and manpower. It is often accomplished through software testing at various levels. One such level is unit testing, where the behaviour of individual components is tested. In this [...] Read more.
Asserting program correctness is a longstanding challenge in software development that consumes lots of resources and manpower. It is often accomplished through software testing at various levels. One such level is unit testing, where the behaviour of individual components is tested. In this paper, we introduce the concept of test analysis, which instead of executing unit tests, analyses them to establish their outcome. This is line with previous approaches towards using formal methods for program verification; however, we introduce a middle layer called the test analysis framework, which allows for the introduction of new capabilities. We (briefly) formalize ordinary testing and test analysis to define the relation between the two. We introduce the notion of rich tests with a syntax and semantic instantiated for C. A prototype framework is implemented and extended to handle property-based stubbing and non-deterministic string variables. A few select examples are presented to demonstrate the capabilities of the framework. Full article
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19 pages, 5507 KB  
Article
RoboDeploy: A Metamodel-Driven Framework for Automated Multi-Host Docker Deployment of ROS 2 Systems in IoRT Environments
by Miguel Ángel Barcelona, Laura García-Borgoñón, Pablo Torner and Ariadna Belén Ruiz
Software 2026, 5(1), 1; https://doi.org/10.3390/software5010001 - 19 Dec 2025
Viewed by 405
Abstract
Robotic systems increasingly operate in complex and distributed environments, where software deployment and orchestration pose major challenges. This paper presents a model-driven approach that automates the containerized deployment of robotic systems in Internet of Robotic Things (IoRT) environments. Our solution integrates Model-Driven Engineering [...] Read more.
Robotic systems increasingly operate in complex and distributed environments, where software deployment and orchestration pose major challenges. This paper presents a model-driven approach that automates the containerized deployment of robotic systems in Internet of Robotic Things (IoRT) environments. Our solution integrates Model-Driven Engineering (MDE) with containerization technologies to improve scalability, reproducibility, and maintainability. A dedicated metamodel introduces high-level abstractions for describing deployment architectures, repositories, and container configurations. A web-based tool enables collaborative model editing, while an external deployment automator generates validated Docker and Compose artifacts to support seamless multi-host orchestration. We validated the approach through real-world experiments, which show that the method effectively automates deployment workflows, ensures consistency across development and production environments, and significantly reduces configuration effort. These results demonstrate that model-driven automation can bridge the gap between Software Engineering (SE) and robotics, enabling Software-Defined Robotics (SDR) and supporting scalable IoRT applications. Full article
(This article belongs to the Topic Software Engineering and Applications)
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