Science of Computer Programming最新文献

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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 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

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

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 ACInv, 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.

自动程序验证一直是构建可靠软件的重要组成部分。虽然循环分析仍然是一个理论挑战，但循环不变量分析的自动化有效地解决了这个问题。然而，现有的不变量生成工具主要对纯数值结构或纯基于指针结构的程序有效。现实世界的程序经常混合复杂的数据结构和控制流。这些结构可以包括数组、指针和递归定义，而控制流可能涉及多个嵌套或并发循环。传统方法一般只对简单的数值程序或特定的段生成不变量，缺乏广泛的适用性。为了自动生成现实世界程序的循环不变量，我们提出了ACInv，一个自动复杂程序循环不变量生成工具，它结合了静态分析和大型语言模型（LLM）的提示来生成适当的循环不变量。我们采用静态分析系统地分解程序的数据结构和循环。这涉及到将有关变量、数值数据和完整循环结构的结构信息逐层传输到LLM，从而能够生成相应的不变量。与AutoSpec之前的工作相比，我们更深入地研究了每个循环中的变量信息。我们在ACInv上进行了实验，结果表明ACInv在具有数据结构的数据集上优于以前的工具，并且在没有数据结构的数值程序上保持与最先进的工具AutoSpec相似的性能。对于整个数据集，ACInv可以比AutoSpec多解决21%的示例，并且可以生成参考数据结构模板。

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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 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Science of Computer Programming

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

Cuauhtémoc López-Martín

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.

软件设计是软件开发生命周期（SDLC）中的一项独特活动。它通常由一个独立的、专门的团队承担，其预算依赖于所需的人-小时预测（即工作量）。过度预测可能导致项目在开始之前被拒绝，而预测不足可能导致项目在完成之前取消。通过活动预测工作量百分比的常见做法包括从总SDLC工作量中计算工作量百分比。然而，使用这种方法报道的设计努力从0.62%到50.35%不等。另一种做法是使用预测模型；然而，直到2025年发表的系统文献综述表明，通过解决全部SDLC工作的现有模型，缺乏专门应用于软件设计工作预测（SDEP）的模型。因此，本研究将支持向量回归（SVR）和多层感知器（MLP）神经网络两种模型应用于SDEP，这两种模型在努力预测领域中被认为是最准确的。通过遗传算法对其参数进行优化，并与统计回归模型（SRM）的性能进行比较。所有的模型都是在七个数据集上进行训练的，这些数据集是从软件项目的国际公共存储库中选择的，这些存储库用于几十项软件工作预测的研究。结果表明，SVR在五个数据集上的表现优于SRM，在其余两个数据集上表现相同。MLP在三个数据集上优于SRM，在其余四个数据集上表现相同。因此，MLP和SVR都可以用于SDEP。

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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 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Science of Computer Programming

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

Fatimah Rita Ahmadi

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,

Ma t_{k}

. 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

T_{i j}

are interpreted as 1-morphisms and tensors with four indices

T_{i j k l}

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 2Vec, as introduced by Kapranov and Voevodsky.

为了形式化线性代数中的计算，以开发有效的算法和适合函数式编程语言和更快的并行计算的框架，我们采用了一种方法，将线性代数结构（如矩阵）视为矩阵（Matk）类别中的态射。我们进一步将这个框架推广到任意一元半加性范畴。为了扩展这一观点并纳入高秩矩阵（张量），我们引入了半加性2-范畴的概念，其中矩阵Tij被解释为1-态射，具有四指标的张量Tijkl被解释为2-态射。这种形式化为线性代数提供了一个无索引的类型化框架，它可以自然地容纳具有最多四个指标的矩阵和张量。此外，我们将框架扩展到一元半可加的2范畴，并演示了由Kapranov和Voevodsky引入的2Vec的2范畴内的显式运算和向量化技术。

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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 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

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

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.

游戏开发者一直在广泛采用软件设计模式（sdp），因为它们在为软件产品提供可维护性、可伸缩性和适应性方面非常有用。然而，它们在严肃游戏（SG）背景下的认知情感训练中的系统应用尚未被探索。反过来，认知-情感模型（COGAF）的定义有助于他们在训练用户认知和情感方面的使用。本文的重点是将sdp集成到COGAF模型中，以促进SGs的开发。本文提出了基于COGAF模型的四种简化SG开发的设计模式。COGAF模型中使用的sdp创建了一组Java类，这些类体现了用于认知情感训练的严肃游戏设计模式（Serious Game Design Patterns， SGDP）的架构原则。在COGAF中集成设计模式（模板方法、工厂方法、组合和策略）有助于在SGs中组织、重用和维护代码，从而降低开发复杂性。我们通过四种方式来验证我们的建议，第一种是关于《SGs》用户互动的案例研究，第二种是执行能力问题，第三种是通过评估可用性和主观玩家体验，第四种是使用扩展和经典COGAF来评估《SGs》开发者的开发时间。结果表明，将设计模式集成到COGAF中可以改善代码组织，从而改善SG开发人员的开发时间。反过来，生成的认知情感训练的质量和有效性不受影响，确保了建议的连贯性和完整性。

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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 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

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)

引用次数: 0

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 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

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 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

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

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.

网络物理系统（CPS）的不断发展，特别是由物联网（IoT）支持的网络物理系统（CPS），导致了大量数据的产生，以及对智能、实时决策的需求不断增长。在这样的系统中，高效的基于软件的任务调度对于确保及时响应、最佳资源利用和持续的服务质量（QoS）至关重要。异构多服务器架构通过支持并行处理和自适应工作负载分配，提供了一个很有前途的平台。然而，计算节点固有的异构性，加上时间敏感应用程序严格的时间要求，对软件级调度机制提出了实质性的挑战。为了解决这些挑战，本文介绍了一种专门为包含多个输入队列和异构服务器的CPS环境设计的利润感知和自适应调度算法。该算法利用贪婪启发式和基于效用的任务建模方法，根据当前系统状态和任务期限动态分配资源。在截止日期内完成的任务对系统利润有积极的贡献，而延迟的任务则会受到惩罚或被拒绝。大量的仿真实验表明，该算法显著提高了系统的响应能力，提高了系统的整体利用率。在对基线和完善的调度策略（包括随机、轮循、MaxWeight和MaxWeight与折扣ucb）的比较评估中，所提出的方法实现了平均响应时间减少30%，总利润增加31%，截止日期满意率提高50%。这些结果突出了所提出的软件级调度方法在时间敏感环境下提高CPS运行效率的有效性。

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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 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

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.

Mission-time Linear Temporal Logic (MLTL) is a finite, discrete, closed-interval-bounded variant of Metric Temporal Logic (MTL) that formal methods practitioners use to specify requirements for safety-critical systems, such as aircraft and spacecraft. Our tool addresses the specification bottleneck of formal verification by providing an interactive visualization tool for MLTL that allows practitioners to validate that their MLTL specifications do indeed match the intended requirements. We provide an overview of the functionalities of the command-line interface and the graphical user interface of the WEST tool. Additionally, we provide five independent methods used to validate the tool's correctness, as well as experimental results demonstrating the tool's scalability on three suites of randomly generated MLTL formulas.

任务时间线性时间逻辑（MLTL）是度量时间逻辑（MTL）的一种有限的、离散的、闭区间有界的变体，正式方法实践者使用它来指定安全关键系统（如飞机和航天器）的需求。我们的工具通过为MLTL提供一个交互式可视化工具来解决正式验证的规范瓶颈，该工具允许从业者验证他们的MLTL规范确实符合预期的需求。我们概述了WEST工具的命令行界面和图形用户界面的功能。此外，我们提供了五种独立的方法来验证工具的正确性，以及实验结果，证明了该工具在三组随机生成的MLTL公式上的可扩展性。

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

IF 1.5 4区计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Science of Computer Programming

Pub Date : 2026-01-01 Epub Date: 2025-05-02 DOI: 10.1016/j.scico.2025.103321

Dominique Méry, Rosemary Monahan

引用次数: 0

Random test generators demystified: Differences and potential for compiler reliability 揭秘随机测试生成器：编译器可靠性的差异和潜力

IF 1.5 4区计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Science of Computer Programming

Pub Date : 2026-01-01 Epub Date: 2025-07-03 DOI: 10.1016/j.scico.2025.103359

Yang Wang, Zeyu Lu, Beining Wu, Yibiao Yang, Hongmin Lu, Yuming Zhou

Compiler testing requires diverse programs as inputs. Various random program generators that can produce programs from scratch have been developed for this purpose. However, there is a gap in understanding (1) the differences among the generated programs and (2) how to make better use of these generators. To fill this gap, we selected five C random program generators and conducted the first comprehensive empirical analysis. For generated programs, our study focuses on three key areas: comparing the variations in features from multiple perspectives, analyzing the impact of compiling these programs on open-source compilers, and exploring their application potential in non-traditional testing scenarios. Programs from different generators show distinctive differences in various program features. Each has unique abilities to increase coverage of specific compiler components. Moreover, they can spot inconsistencies in the coverage statistics provided by different compilers, indicating promising application potential. Our study demonstrates that existing generators involve trade-offs in their design, making it challenging for any single implementation to balance efficiency, usability, and diversity for all scenarios. This motivates us to both maximize the potential of current generators and innovate to create more high-quality test programs for modern compiler quality assurance.

编译器测试需要不同的程序作为输入。为了这个目的，已经开发了各种可以从头开始生成程序的随机程序生成器。然而，在理解(1)生成的程序之间的差异和(2)如何更好地利用这些生成器方面存在差距。为了填补这一空白，我们选择了五个C随机程序生成器，并进行了第一次全面的实证分析。对于生成的程序，我们的研究主要集中在三个关键领域：从多个角度比较特征的变化，分析编译这些程序对开源编译器的影响，以及探索它们在非传统测试场景中的应用潜力。来自不同生成器的程序在各种程序特性上表现出明显的差异。每个都有独特的能力来增加特定编译器组件的覆盖率。此外，它们还可以发现不同编译器提供的覆盖率统计数据中的不一致之处，从而表明应用程序的潜力。我们的研究表明，现有的生成器在其设计中涉及权衡，使得任何单一实现都难以平衡所有场景的效率，可用性和多样性。这促使我们最大化当前生成器的潜力，并为现代编译器质量保证创造更多高质量的测试程序。

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引用次数: 0

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