Science of Computer Programming最新文献_第5页

Enhancing automated loop invariant generation for complex programs with large language models 增强具有大型语言模型的复杂程序的自动循环不变量生成

IF 1.4 4区计算机科学

Science of Computer Programming Pub Date : 2026-03-01 Epub Date: 2025-08-29 DOI: 10.1016/j.scico.2025.103387

Ruibang Liu, Minyu Chen, Ling-I Wu, Jingyu Ke, Guoqiang Li

{"title":"Enhancing automated loop invariant generation for complex programs with large language models","authors":"Ruibang Liu, Minyu Chen, Ling-I Wu, Jingyu Ke, Guoqiang Li","doi":"10.1016/j.scico.2025.103387","DOIUrl":"10.1016/j.scico.2025.103387","url":null,"abstract":"<div><div>Automated program verification has always been an important component of building trustworthy software. While the analysis of loops remains a theoretical challenge, the automation of loop invariant analysis has effectively resolved the problem. However, existing invariant generation tools are predominantly effective for programs with purely numerical or purely pointer-based structures. Real-world programs often mix complex data structures and control flows. These structures can include arrays, pointers, and recursive definitions, while control flows may involve multiple nested or concurrent loops. Traditional methods generally only generate invariants for simple numerical programs or specific segments, lacking broad applicability. In order to automatically generate loop invariants for real-world programs, we proposed <em>ACInv</em>, an Automated Complex program loop Invariant generation tool, which combines static analysis with prompting with Large Language Models (LLM) to generate the proper loop invariants. We employ static analysis to systematically decompose the program's data structures and loops. This involves layer-by-layer transmission of structural information about variables, numerical data, and the complete loop structure to the LLM, enabling the generation of corresponding invariants. In comparison to prior work on AutoSpec, we delve deeper into the variable information within each loop. We conducted experiments on ACInv, which showed that ACInv outperformed previous tools on data sets with data structures and maintained similar performance to the state-of-the-art tool AutoSpec on numerical programs without data structures. For the total data set, ACInv can solve 21% more examples than AutoSpec, and can generate reference data structure templates.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"248 ","pages":"Article 103387"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning models for predicting software design effort 用于预测软件设计工作的机器学习模型

IF 1.4 4区计算机科学

Science of Computer Programming Pub Date : 2026-03-01 Epub Date: 2025-08-25 DOI: 10.1016/j.scico.2025.103385

Cuauhtémoc López-Martín

{"title":"Machine learning models for predicting software design effort","authors":"Cuauhtémoc López-Martín","doi":"10.1016/j.scico.2025.103385","DOIUrl":"10.1016/j.scico.2025.103385","url":null,"abstract":"<div><div>Software design is a distinctive activity within the software development life cycle (SDLC). It is typically undertaken by an independent, specialized team, whose budget relies on the required person-hours prediction (i.e., effort). An over-prediction could cause project rejection before starting, whereas an under-prediction may result project cancellation before completion. A common practice for predicting the effort percentage by activity involves calculating it from the total SDLC effort. However, the reported design effort using this method varies widely from 0.62% to 50.35%. Another practice involves using prediction models; however, systematic literature reviews published up to 2025 indicate the absence of models specifically applied for software design effort prediction (SDEP) by existing only models addressing the total SDLC effort. Thus, the present study applies two models to SDEP reported as most accurate in the effort prediction field: support vector regression (SVR), and Multi-layer perceptron (MLP) neural network. Their parameters are optimized through genetic algorithms, and their performance is compared to that of a statistical regression model (SRM). All models were trained on seven data sets selected from an international public repository of software projects used in dozens of studies on software effort prediction. Results show that SVR performed statistically better than SRM in five data sets and equally on the remaining two. MLP outperformed SRM on three data sets and equally on the resting four. Consequently, both MLP and SVR can be used to SDEP.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"248 ","pages":"Article 103385"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Typing tensor calculus in 2-categories (I) 2类张量微积分的类型化(I)

IF 1.4 4区计算机科学

Science of Computer Programming Pub Date : 2026-03-01 Epub Date: 2025-07-29 DOI: 10.1016/j.scico.2025.103376

Fatimah Rita Ahmadi

{"title":"Typing tensor calculus in 2-categories (I)","authors":"Fatimah Rita Ahmadi","doi":"10.1016/j.scico.2025.103376","DOIUrl":"10.1016/j.scico.2025.103376","url":null,"abstract":"<div><div>To formalize calculations in linear algebra for the development of efficient algorithms and a framework suitable for functional programming languages and faster parallel computations, we adopt an approach that treats linear algebraic structures, such as matrices, as morphisms in the category of matrices, <span><math><mi>Ma</mi><msub><mrow><mi>t</mi></mrow><mrow><mi>k</mi></mrow></msub></math></span>. We further generalize this framework to arbitrary monoidal semiadditive categories. To extend this perspective and incorporate higher-rank matrices (tensors), we introduce the notion of semiadditive 2-categories, where matrices <span><math><msub><mrow><mi>T</mi></mrow><mrow><mi>i</mi><mi>j</mi></mrow></msub></math></span> are interpreted as 1-morphisms and tensors with four indices <span><math><msub><mrow><mi>T</mi></mrow><mrow><mi>i</mi><mi>j</mi><mi>k</mi><mi>l</mi></mrow></msub></math></span> as 2-morphisms. This formalization provides an index-free, typed framework for linear algebra that naturally accommodates matrices and tensors with up to four indices. Moreover, we extend the framework to monoidal semiadditive 2-categories and demonstrate explicit operations and vectorization techniques within the 2-category of <strong>2Vec</strong>, as introduced by Kapranov and Voevodsky.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"248 ","pages":"Article 103376"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design patterns applied in the development of serious games for cognitive-affective training 设计模式在认知情感训练严肃游戏开发中的应用

IF 1.4 4区计算机科学

Science of Computer Programming Pub Date : 2026-03-01 Epub Date: 2025-08-05 DOI: 10.1016/j.scico.2025.103378

Claudia Gómez Llanez , Paola Vallejo , Jose Aguilar

{"title":"Design patterns applied in the development of serious games for cognitive-affective training","authors":"Claudia Gómez Llanez , Paola Vallejo , Jose Aguilar","doi":"10.1016/j.scico.2025.103378","DOIUrl":"10.1016/j.scico.2025.103378","url":null,"abstract":"<div><div>Game developers have been widely adopting software design patterns (SDPs) for their usefulness in providing maintainability, scalability, and adaptability to their software products. Nevertheless, their systematic application to cognitive-affective training in the context of Serious Games (SG) has not been explored. In turn, the definition of a Cognitive-Affective model (COGAF) for SGs facilitates their use in training cognitive and affective aspects in users. This article focuses on integrating SDPs into the COGAF model to facilitate the development of SGs. The paper presents four design patterns to simplify SG development based on the COGAF model. The SDPs used in the COGAF model create a set of Java classes, which embody the architectural principles of Serious Game Design Patterns (SGDP) for cognitive-affective training. Integrating design patterns (Template Method, Factory Method, Composite, and Strategy) in COGAF contributes to the organization, reuse and maintenance of code in SGs, reducing development complexity. We validate our proposal in four ways, the first with a case study involving user interaction in SGs, the second by implementing competency questions, the third, by assessing the usability and subjective player experience, and the fourth is a statistical evaluation to assess the development times of SG developers using extended and classic COGAF. The results show that integrating design patterns into COGAF improves code organization, which translates into improved development times for SG developers. In turn, the quality and effectiveness of cognitive-affective training with the generated SGs are not affected, ensuring the coherence and completeness of the proposal.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"248 ","pages":"Article 103378"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preface for “Selected Papers from the 27th Ibero-American Conference on Software Engineering (CIbSE 2024)” “第27届伊比利亚-美洲软件工程会议（CIbSE 2024）论文选集”前言

IF 1.4 4区计算机科学

Science of Computer Programming Pub Date : 2026-03-01 Epub Date: 2025-08-31 DOI: 10.1016/j.scico.2025.103388

Edson Oliveira Jr (Guest Editors) , Ignacio García Rodríguez de Guzmán (Guest Editors) , Marcela Genero (Guest Editors) , Beatriz Marín (Guest Editors) , Guilherme Travassos (Guest Editors)

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Special issue on selected papers from the 19th International Conference on Formal Aspects of Component Software (FACS 2023) 第19届组件软件正式方面国际会议（FACS 2023）论文精选特刊

IF 1.4 4区计算机科学

Science of Computer Programming Pub Date : 2026-03-01 Epub Date: 2025-09-11 DOI: 10.1016/j.scico.2025.103391

引用次数: 0

Profit-aware scheduling for time-sensitive applications in heterogeneous multi-server systems 异构多服务器系统中时间敏感应用程序的利润感知调度

IF 1.4 4区计算机科学

Science of Computer Programming Pub Date : 2026-03-01 Epub Date: 2025-09-10 DOI: 10.1016/j.scico.2025.103390

Rezvan Salimi, Sadoon Azizi, Amir Rastegari

{"title":"Profit-aware scheduling for time-sensitive applications in heterogeneous multi-server systems","authors":"Rezvan Salimi, Sadoon Azizi, Amir Rastegari","doi":"10.1016/j.scico.2025.103390","DOIUrl":"10.1016/j.scico.2025.103390","url":null,"abstract":"<div><div>The continuous evolution of Cyber-Physical Systems (CPS), particularly those empowered by the Internet of Things (IoT), has led to the generation of massive volumes of data and a growing demand for intelligent, real-time decision-making. Within such systems, efficient software-based task scheduling is vital to ensure timely responsiveness, optimal resource utilization, and sustained Quality of Service (QoS). Heterogeneous multi-server architectures offer a promising platform by enabling parallel processing and adaptive workload distribution. However, the inherent heterogeneity of computational nodes, coupled with the stringent temporal requirements of time-sensitive applications, imposes substantial challenges on software-level scheduling mechanisms. To address these challenges, this paper introduces a profit-aware and adaptive scheduling algorithm specifically designed for CPS environments comprising multiple input queues and heterogeneous servers. The proposed algorithm utilizes a greedy heuristic and a utility-based task modeling approach to dynamically allocate resources in accordance with current system state and task deadlines. Tasks that are completed within their deadline thresholds contribute positively to system profit, whereas delayed tasks incur penalties or are rejected. Extensive simulation experiments demonstrate that the proposed algorithm significantly enhances system responsiveness and improves overall system utility. In comparative evaluations against baseline and well-established scheduling strategies—including Random, Round Robin, MaxWeight, and MaxWeight with Discounted UCB—the proposed method achieves up to 30% reduction in average response time, 31% increase in total profit, and 50% improvement in deadline satisfaction rate. These results highlight the effectiveness of the proposed software-level scheduling approach in enhancing the operational efficiency of CPS in time-sensitive contexts.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"248 ","pages":"Article 103390"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

WEST: Interactive validation of Mission-time Linear Temporal Logic (MLTL) 任务时间线性时间逻辑（MLTL）的交互验证

IF 1.5 4区计算机科学

Science of Computer Programming Pub Date : 2026-03-01 Epub Date: 2025-07-15 DOI: 10.1016/j.scico.2025.103365

Wang Zili, Gamboa Guzman Laura P., Rozier Kristin Y.

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Earley table traversing parsers Earley表遍历分析器

IF 1.5 4区计算机科学

Science of Computer Programming Pub Date : 2026-01-01 Epub Date: 2025-05-28 DOI: 10.1016/j.scico.2025.103335

Elizabeth Scott, Adrian Johnstone

引用次数: 0

Selected papers from the Rigorous State-Based Methods, 7th International Conference, ABZ 2023, Nancy, France, May 30–June 2, 2023 论文选自《严格的基于状态的方法》，第七届国际会议，ABZ 2023, Nancy, France， 2023年5月30日至6月2日