Pub Date : 2025-12-13DOI: 10.1016/j.scico.2025.103431
Zahra Mohaghegh Rad, Ehsan Khamespanah
Smart home environments receive substantial improvement from the Internet of Things (IoT) through automated systems and connected devices, which optimize living space management. The advanced technology of smart homes requires strong anomaly detection systems together with Root Cause Analysis (RCA) to maintain security and reliability. This paper presents a new immune system model for smart homes that detects unusual behavior patterns and conducts full RCA. Our methodology uses the Actor Model together with deep learning approaches that process sensor events while applying causal inference to detect anomalies and their root causes. We use multiple deep learning architectures, including sequence-to-sequence (Seq2Seq), autoencoder, and LSTM networks, to detect various anomalies, which include missing data and abnormal data values. Our autoencoder-based solution demonstrates superior performance, achieving 96.2 % precision and a 98.0 % F1-score. These results represent a significant improvement of 76.8 % in precision and 73.7 % in F1-score over state-of-the-art baseline methods. Our research demonstrates how advanced techniques improve both anomaly detection accuracy and the efficiency of RCA, which results in better smart home environment reliability and resilience.
{"title":"Towards developing an actor-based immune system for smart homes","authors":"Zahra Mohaghegh Rad, Ehsan Khamespanah","doi":"10.1016/j.scico.2025.103431","DOIUrl":"10.1016/j.scico.2025.103431","url":null,"abstract":"<div><div>Smart home environments receive substantial improvement from the Internet of Things (IoT) through automated systems and connected devices, which optimize living space management. The advanced technology of smart homes requires strong anomaly detection systems together with Root Cause Analysis (RCA) to maintain security and reliability. This paper presents a new immune system model for smart homes that detects unusual behavior patterns and conducts full RCA. Our methodology uses the Actor Model together with deep learning approaches that process sensor events while applying causal inference to detect anomalies and their root causes. We use multiple deep learning architectures, including sequence-to-sequence (Seq2Seq), autoencoder, and LSTM networks, to detect various anomalies, which include missing data and abnormal data values. Our autoencoder-based solution demonstrates superior performance, achieving 96.2 % precision and a 98.0 % F1-score. These results represent a significant improvement of 76.8 % in precision and 73.7 % in F1-score over state-of-the-art baseline methods. Our research demonstrates how advanced techniques improve both anomaly detection accuracy and the efficiency of RCA, which results in better smart home environment reliability and resilience.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"251 ","pages":"Article 103431"},"PeriodicalIF":1.4,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808530","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}
Pub Date : 2025-12-06DOI: 10.1016/j.scico.2025.103417
Jan-Christoph Kassing , Jürgen Giesl
Dependency pairs are one of the most powerful techniques to analyze termination of term rewrite systems automatically. We adapt dependency pairs to the probabilistic setting and develop an annotated dependency pair framework for automatically proving almost-sure termination of probabilistic term rewrite systems, both for full and innermost rewriting. To evaluate its power, we implemented our framework in the tool AProVE.
{"title":"The annotated dependency pair framework for almost-sure termination of probabilistic term rewriting","authors":"Jan-Christoph Kassing , Jürgen Giesl","doi":"10.1016/j.scico.2025.103417","DOIUrl":"10.1016/j.scico.2025.103417","url":null,"abstract":"<div><div>Dependency pairs are one of the most powerful techniques to analyze termination of term rewrite systems automatically. We adapt dependency pairs to the probabilistic setting and develop an <em>annotated dependency pair</em> framework for automatically proving almost-sure termination of <em>probabilistic term rewrite systems</em>, both for full and innermost rewriting. To evaluate its power, we implemented our framework in the tool <span>AProVE</span>.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"251 ","pages":"Article 103417"},"PeriodicalIF":1.4,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842777","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}
Pub Date : 2025-12-05DOI: 10.1016/j.scico.2025.103420
Daniel Fernando Gómez-Barrera, Luccas Rojas Becerra, Juan Pinzón Roncancio, David Ortiz Almanza, Juan Arboleda, Mario Linares-Vásquez, Rubén Francisco Manrique
This paper presents CFFitST, a novel strategy for iteratively fine-tuning sentence embeddings using a pre-trained sentence transformer to enhance classification performance in few-shot settings. The method dynamically adjusts the number and composition of training samples based on internal assessments over the training data. CFFitST was evaluated in the “NLBSE 2024” tool competition, which focused on multi-class classification of GitHub issues. The competition required robust few-shot learning models to classify 300 issues across five different repositories. Our approach achieved an F1 score of 84.2 %, representing a 2.44 % statistical significant improvement over the SetFit baseline.
{"title":"CFFitST: Classification few-shot fit sentence transformer","authors":"Daniel Fernando Gómez-Barrera, Luccas Rojas Becerra, Juan Pinzón Roncancio, David Ortiz Almanza, Juan Arboleda, Mario Linares-Vásquez, Rubén Francisco Manrique","doi":"10.1016/j.scico.2025.103420","DOIUrl":"10.1016/j.scico.2025.103420","url":null,"abstract":"<div><div>This paper presents CFFitST, a novel strategy for iteratively fine-tuning sentence embeddings using a pre-trained sentence transformer to enhance classification performance in few-shot settings. The method dynamically adjusts the number and composition of training samples based on internal assessments over the training data. CFFitST was evaluated in the “NLBSE 2024” tool competition, which focused on multi-class classification of GitHub issues. The competition required robust few-shot learning models to classify 300 issues across five different repositories. Our approach achieved an F1 score of 84.2 %, representing a 2.44 % statistical significant improvement over the SetFit baseline.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"250 ","pages":"Article 103420"},"PeriodicalIF":1.4,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796894","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}
Pub Date : 2025-11-22DOI: 10.1016/j.scico.2025.103419
Bin Hu , Lizhi Zheng , Dongjin Yu , Yijian Wu , Jie Chen , Tianyi Hu
Code clones have been a hot topic in software engineering for decades. Due to the rapid development of clone detection techniques, it is not difficult to find code clones in software systems, while managing the vast amounts of clones remains an open problem. Typically, we should adopt refactoring approaches to eliminate clones, thereby mitigating the threat to software maintenance. In some situations, the clone group may contain several different code variants that reside in different locations, thus making refactoring too complicated, as their differences must be analyzed and reconciled before refactoring. Therefore, we should find an approach to recognize clone groups that are easy to refactor or eliminate. In this paper, we first collected large-scale datasets from three different domains and studied the distribution of four different metrics of code clones. We found that the distribution of each metric follows a certain pattern, the number of inner file clone accounts for approximately 50 %, the number of Type3 clone accounts for above 45 %. But we cannot judge the complexity of code clone groups based solely on these metrics. Based on our findings, we propose a classification approach to assist developers to find clone groups that are easy to eliminate by refactoring from those that are hard to refactor. We propose four different clone feature entropy measures based on information entropy theory, including variant entropy, distribution entropy, relation entropy, and syntactic entropy. Then, we calculate fused clone entropy, which is the weighted summation of the above four clone feature entropy. Finally, we use the four types of feature entropy and the fused feature entropy to classify or rank code clone groups. Experiments on three different application domains show that the proposed clone feature entropy can help developers identify clone groups that are easy to eliminate by refactoring. Manual validation also reveals that the complexity of clone groups is not solely dependent on the number of clone instances. This approach provides a new way to manage code clones and offers some useful ideas for future clone maintenance research.
{"title":"Code clone classification based on multi-dimension feature entropy","authors":"Bin Hu , Lizhi Zheng , Dongjin Yu , Yijian Wu , Jie Chen , Tianyi Hu","doi":"10.1016/j.scico.2025.103419","DOIUrl":"10.1016/j.scico.2025.103419","url":null,"abstract":"<div><div>Code clones have been a hot topic in software engineering for decades. Due to the rapid development of clone detection techniques, it is not difficult to find code clones in software systems, while managing the vast amounts of clones remains an open problem. Typically, we should adopt refactoring approaches to eliminate clones, thereby mitigating the threat to software maintenance. In some situations, the clone group may contain several different code variants that reside in different locations, thus making refactoring too complicated, as their differences must be analyzed and reconciled before refactoring. Therefore, we should find an approach to recognize clone groups that are easy to refactor or eliminate. In this paper, we first collected large-scale datasets from three different domains and studied the distribution of four different metrics of code clones. We found that the distribution of each metric follows a certain pattern, the number of inner file clone accounts for approximately 50 %, the number of Type3 clone accounts for above 45 %. But we cannot judge the complexity of code clone groups based solely on these metrics. Based on our findings, we propose a classification approach to assist developers to find clone groups that are easy to eliminate by refactoring from those that are hard to refactor. We propose four different clone feature entropy measures based on information entropy theory, including variant entropy, distribution entropy, relation entropy, and syntactic entropy. Then, we calculate fused clone entropy, which is the weighted summation of the above four clone feature entropy. Finally, we use the four types of feature entropy and the fused feature entropy to classify or rank code clone groups. Experiments on three different application domains show that the proposed clone feature entropy can help developers identify clone groups that are easy to eliminate by refactoring. Manual validation also reveals that the complexity of clone groups is not solely dependent on the number of clone instances. This approach provides a new way to manage code clones and offers some useful ideas for future clone maintenance research.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"250 ","pages":"Article 103419"},"PeriodicalIF":1.4,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693880","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}
Pub Date : 2025-11-21DOI: 10.1016/j.scico.2025.103416
Michael Hanus
Unintended failures during a computation are painful but frequent during software development. Failures due to external reasons (e.g., missing files, no permissions, etc.) can be caught by exception handlers. Programming failures, such as calling a partially defined operation with unintended arguments, are often not caught due to the assumption that the software is correct. This paper presents an approach to verify such assumptions. For this purpose, non-failure conditions for operations are inferred and then checked in all uses of partially defined operations. In the positive case, the absence of such failures is ensured. In the negative case, the programmer could adapt the program to handle possibly failing situations and check the program again. Our method is fully automatic and can be applied to larger declarative programs. The results of an implementation for functional logic Curry programs are presented.
{"title":"Inferring non-failure conditions for declarative programs","authors":"Michael Hanus","doi":"10.1016/j.scico.2025.103416","DOIUrl":"10.1016/j.scico.2025.103416","url":null,"abstract":"<div><div>Unintended failures during a computation are painful but frequent during software development. Failures due to external reasons (e.g., missing files, no permissions, etc.) can be caught by exception handlers. Programming failures, such as calling a partially defined operation with unintended arguments, are often not caught due to the assumption that the software is correct. This paper presents an approach to verify such assumptions. For this purpose, non-failure conditions for operations are inferred and then checked in all uses of partially defined operations. In the positive case, the absence of such failures is ensured. In the negative case, the programmer could adapt the program to handle possibly failing situations and check the program again. Our method is fully automatic and can be applied to larger declarative programs. The results of an implementation for functional logic Curry programs are presented.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"250 ","pages":"Article 103416"},"PeriodicalIF":1.4,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693875","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}
Pub Date : 2025-11-15DOI: 10.1016/j.scico.2025.103397
Oleg Kiselyov
MetaOCaml is a superset of OCaml for convenient code generation with static guarantees: the generated code is well-formed, well-typed and well-scoped, by construction. Not only the produced code always compiles; code fragments with a variable escaping its scope are detected already during code generation. MetaOCaml has been employed for compiling domain-specific languages, generic programming, automating tedious specializations in high-performance computing, generating efficient computational kernels and embedded programming. It is used in education, and served as inspiration for several other metaprogramming systems.
Most well-known in MetaOCaml are the types for values representing generated code and the template-based mechanism to produce such values, a.k.a., brackets and escapes. MetaOCaml also features cross-stage persistence, generating ordinary and mutually-recursive definitions, first-class pattern-matching and heterogeneous metaprogramming.
The extant implementation of MetaOCaml, first presented at FLOPS 2014, has been continuously evolving. We describe the current design and implementation, stressing particularly notable additions. Among them is a new and efficient translation from typed code templates to code combinators. Scope extrusion detection unexpectedly brought let- insertion, and a conclusive solution to the 20-year–old vexing problem of cross-stage persistence.
{"title":"MetaOCaml: ten years later – System description","authors":"Oleg Kiselyov","doi":"10.1016/j.scico.2025.103397","DOIUrl":"10.1016/j.scico.2025.103397","url":null,"abstract":"<div><div>MetaOCaml is a superset of OCaml for convenient code generation with static guarantees: the generated code is well-formed, well-typed and well-scoped, by construction. Not only the produced code always compiles; code fragments with a variable escaping its scope are detected already during code generation. MetaOCaml has been employed for compiling domain-specific languages, generic programming, automating tedious specializations in high-performance computing, generating efficient computational kernels and embedded programming. It is used in education, and served as inspiration for several other metaprogramming systems.</div><div>Most well-known in MetaOCaml are the types for values representing generated code and the template-based mechanism to produce such values, a.k.a., brackets and escapes. MetaOCaml also features cross-stage persistence, generating ordinary and mutually-recursive definitions, first-class pattern-matching and heterogeneous metaprogramming.</div><div>The extant implementation of MetaOCaml, first presented at FLOPS 2014, has been continuously evolving. We describe the current design and implementation, stressing particularly notable additions. Among them is a new and efficient translation from typed code templates to code combinators. Scope extrusion detection unexpectedly brought let- insertion, and a conclusive solution to the 20-year–old vexing problem of cross-stage persistence.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"250 ","pages":"Article 103397"},"PeriodicalIF":1.4,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796893","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}
Pub Date : 2025-11-14DOI: 10.1016/j.scico.2025.103414
Rafaela Almeida , Sidney Nogueira , Augusto Sampaio
Testing concurrent systems is challenging due to their complex interactions and behaviours, along with the difficulty in reproducing failures. We propose a sound strategy for testing concurrent mobile applications by extracting use cases that capture interleavings of behaviours of existing test cases for individual features. These use cases are then used to create a formal model that is the input for a refinement checking approach to generate test cases that are still sequential but exercise the execution of concurrent features. We introduce a conformance relation, cspioq, which considers quiescent behaviour (absence of output). This relation is based on cspio (which is itself inspired by ioco); cspio does not take quiescence behaviour into account. While ioco as well as cspioco (a denotational semantics for ioco based on CSP) rely on suspension traces, our approach adopts the traces model annotated with a special event to represent quiescence. This allowed us to reuse our previous theory and test case generation strategy for sequential systems in a conservative way. We also analyse the complexity of automatically generating test cases. For implementation efficiency, we optimise the strategy by directly interleaving steps of existing test cases and show that this preserves soundness. Moreover, we provide tool support for every phase of the approach. Finally, we present the results of an empirical evaluation designed to measure the effectiveness of the overall strategy in terms of test coverage and bug detection. The results indicate that our approach yields higher coverage and higher bug detection rates compared to the set of tests originally developed by our industrial partner (Motorola) engineers.
{"title":"Combining sequential feature test cases to generate sound tests for concurrent features","authors":"Rafaela Almeida , Sidney Nogueira , Augusto Sampaio","doi":"10.1016/j.scico.2025.103414","DOIUrl":"10.1016/j.scico.2025.103414","url":null,"abstract":"<div><div>Testing concurrent systems is challenging due to their complex interactions and behaviours, along with the difficulty in reproducing failures. We propose a sound strategy for testing concurrent mobile applications by extracting use cases that capture interleavings of behaviours of existing test cases for individual features. These use cases are then used to create a formal model that is the input for a refinement checking approach to generate test cases that are still sequential but exercise the execution of concurrent features. We introduce a conformance relation, <strong>cspio</strong><sub><strong>q</strong></sub>, which considers quiescent behaviour (absence of output). This relation is based on <strong>cspio</strong> (which is itself inspired by <strong>ioco</strong>); <strong>cspio</strong> does not take quiescence behaviour into account. While <strong>ioco</strong> as well as <strong>cspioco</strong> (a denotational semantics for <strong>ioco</strong> based on CSP) rely on suspension traces, our approach adopts the traces model annotated with a special event to represent quiescence. This allowed us to reuse our previous theory and test case generation strategy for sequential systems in a conservative way. We also analyse the complexity of automatically generating test cases. For implementation efficiency, we optimise the strategy by directly interleaving steps of existing test cases and show that this preserves soundness. Moreover, we provide tool support for every phase of the approach. Finally, we present the results of an empirical evaluation designed to measure the effectiveness of the overall strategy in terms of test coverage and bug detection. The results indicate that our approach yields higher coverage and higher bug detection rates compared to the set of tests originally developed by our industrial partner (Motorola) engineers.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"250 ","pages":"Article 103414"},"PeriodicalIF":1.4,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145580046","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}
Pub Date : 2025-11-13DOI: 10.1016/j.scico.2025.103411
Yuzhou Liu , Qi Wang , Shuang Jiang , Runze Wu , Hongxu Tian , Peng Zhang
Context: Many researchers have proposed vulnerability detection methods to enhance software reliability by analyzing the program. However, some vulnerabilities are difficult to be identified only from the source codes, especially the ones related to the execution.
Objectives: To solve this problem, this paper introduces extra binary codes and proposes a novel solution for software vulnerability detection based on the multimodal information fusion.
Methods: The approach treats the source and binary codes as different modalities, and uses two pre-trained models as feature extractors to analyze them separately. Then, we design an attention-based information fusion strategy that taking the information from source codes as the main body while the one from binary codes as the supplement. It could not only capture the correlations among features across different modalities, but also filter the redundancy from the binary codes in the fusion process. In this way, a more comprehensive representation of software is gained and finally taken as the basis for the vulnerability detection.
Results: Our method was comprehensively evaluated on three widely-used datasets in different languages, that is Reveal in C, Devign in C++, and Code_vulnerability_java in Java: (1) For vulnerability detection performance, the Accuracy reached 86.09 %, 84.58 %, and 80.43 % across the three datasets, with F1-scores of 82.87 %, 84.62 %, and 79.58 % respectively; (2) Compared with seven state-of-the-art baseline methods, our approach achieved Accuracy improvements of 2.38 %-3.01 % and F1-score enhancements of 2.32 %-8.47 % across the datasets; (3) Moreover, the ablation experiment shows when combining binary codes with source codes (versus using source codes alone), the Accuracy improved by 6.83 %-13.76 % and F1-score increased by 5.36 %-9.86 %, demonstrating the significant performance gains from multimodal data integration.
Conclusion: The results show that our approach can achieve good performance for the task of software vulnerability detection. Meanwhile, ablation experiments confirm the contributions of binary codes to the detection and indicate the effectiveness of our fusion strategy. We have released the codes and datasets (https://github.com/Wangqxn/Vul-detection) to facilitate follow-up research.
背景：许多研究者提出了漏洞检测方法，通过分析程序来提高软件的可靠性。然而，有些漏洞很难仅从源代码中识别出来，特别是与执行相关的漏洞。为了解决这一问题，本文引入了额外的二进制码，提出了一种基于多模态信息融合的软件漏洞检测新方案。方法：该方法将源码和二进制码视为不同的模态，并使用两个预训练模型作为特征提取器分别对其进行分析。然后，我们设计了一种以源代码信息为主体，以二进制码信息为补充的基于注意力的信息融合策略。该方法不仅可以捕获不同模态特征之间的相关性，而且可以在融合过程中过滤掉二进制码中的冗余。这样可以获得更全面的软件表征，并最终作为漏洞检测的依据。结果：我们的方法在不同语言的3个广泛使用的数据集（Reveal in C、design in c++和Code_vulnerability_java）上进行了综合评价：(1)在漏洞检测性能上，3个数据集的准确率分别达到86.09%、84.58%和80.43%，f1得分分别为82.87%、84.62%和79.58%；(2)与7种最先进的基线方法相比，我们的方法在数据集上的准确率提高了2.38% - 3.01%，f1分数提高了2.32% - 8.47%；(3)此外，烧蚀实验表明，当二进制码与源代码结合使用时（与单独使用源代码相比），准确率提高了6.83% ~ 13.76%，f1分数提高了5.36% ~ 9.86%，显示了多模态数据集成带来的显著性能提升。结论：该方法能够较好地完成软件漏洞检测任务。同时，烧蚀实验证实了二进制码对检测的贡献，表明了我们的融合策略的有效性。我们已经发布了代码和数据集（https://github.com/Wangqxn/Vul-detection），以方便后续研究。
{"title":"Multimodal information fusion for software vulnerability detection based on both source and binary codes","authors":"Yuzhou Liu , Qi Wang , Shuang Jiang , Runze Wu , Hongxu Tian , Peng Zhang","doi":"10.1016/j.scico.2025.103411","DOIUrl":"10.1016/j.scico.2025.103411","url":null,"abstract":"<div><div>Context: Many researchers have proposed vulnerability detection methods to enhance software reliability by analyzing the program. However, some vulnerabilities are difficult to be identified only from the source codes, especially the ones related to the execution.</div><div>Objectives: To solve this problem, this paper introduces extra binary codes and proposes a novel solution for software vulnerability detection based on the multimodal information fusion.</div><div>Methods: The approach treats the source and binary codes as different modalities, and uses two pre-trained models as feature extractors to analyze them separately. Then, we design an attention-based information fusion strategy that taking the information from source codes as the main body while the one from binary codes as the supplement. It could not only capture the correlations among features across different modalities, but also filter the redundancy from the binary codes in the fusion process. In this way, a more comprehensive representation of software is gained and finally taken as the basis for the vulnerability detection.</div><div>Results: Our method was comprehensively evaluated on three widely-used datasets in different languages, that is Reveal in C, Devign in C++, and Code_vulnerability_java in Java: (1) For vulnerability detection performance, the Accuracy reached 86.09 %, 84.58 %, and 80.43 % across the three datasets, with F1-scores of 82.87 %, 84.62 %, and 79.58 % respectively; (2) Compared with seven state-of-the-art baseline methods, our approach achieved Accuracy improvements of 2.38 %-3.01 % and F1-score enhancements of 2.32 %-8.47 % across the datasets; (3) Moreover, the ablation experiment shows when combining binary codes with source codes (versus using source codes alone), the Accuracy improved by 6.83 %-13.76 % and F1-score increased by 5.36 %-9.86 %, demonstrating the significant performance gains from multimodal data integration.</div><div>Conclusion: The results show that our approach can achieve good performance for the task of software vulnerability detection. Meanwhile, ablation experiments confirm the contributions of binary codes to the detection and indicate the effectiveness of our fusion strategy. We have released the codes and datasets <span><span>(https://github.com/Wangqxn/Vul-detection)</span><svg><path></path></svg></span> to facilitate follow-up research.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"250 ","pages":"Article 103411"},"PeriodicalIF":1.4,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624898","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}
Pub Date : 2025-11-05DOI: 10.1016/j.scico.2025.103410
Guisheng Fan , Yuguo Liang , Longfei Zu , Huiqun Yu , Zijie Huang , Wentao Chen
In software development, developers create bug reports within an Issue Tracking System (ITS) to describe the cause, symptoms, severity, and other technical details of bugs. The ITS includes reports of both intrinsic bugs (i.e., those originating within the software itself) and extrinsic bugs (i.e., those arising from third-party dependencies). Although extrinsic bugs are not recorded in the Version Control System (VCS), they can still affect Just-In-Time (JIT) bug prediction models that rely on VCS-derived information.
Previous research has shown that excluding extrinsic bugs can significantly improve JIT bug prediction model’s performance. However, manually classifying intrinsic and extrinsic bugs is time-consuming and prone to errors. To address this issue, we propose a CAN model that integrates the local feature extraction capability of TextCNN with the nonlinear approximation advantage of the Kolmogorov-Arnold Network (KAN). Experiments on 1880 labeled data samples from the OpenStack project demonstrate that the CAN model outperforms benchmark models such as BERT and CodeBERT, achieving an accuracy of 0.7492 and an F1-score of 0.8072. By comparing datasets with and without source code, we find that incorporating source code information enhances model performance. Finally, using the Local Interpretable Model-agnostic Explanations (LIME), an explainable artificial intelligence technique, we identify that keywords such as “test” and “api” in bug reports significantly contribute to the prediction of extrinsic bugs.
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Pub Date : 2025-10-25DOI: 10.1016/j.scico.2025.103398
Arwa Hameed Alsubhi, Ornela Dardha, Simon J. Gay
This paper introduces Coconut, a C++ tool that uses templates for defining object behaviours and validates them with typestate checking. Coconut employs the GIMPLE intermediate representation (IR) from the GCC compiler’s middle-end phase for static checks, ensuring objects follow valid state transitions as defined in typestate templates. It supports features like branching, recursion, aliasing, inheritance, and typestate visualisation. We illustrate Coconut’s application in embedded systems, validating their behaviour pre-deployment. We present an experimental study, showing that Coconut improves performance and reduces code complexity wrt the original code, highlighting the benefits of typestate-based verification.
{"title":"Design and Evaluation of Coconut: Typestates for C++","authors":"Arwa Hameed Alsubhi, Ornela Dardha, Simon J. Gay","doi":"10.1016/j.scico.2025.103398","DOIUrl":"10.1016/j.scico.2025.103398","url":null,"abstract":"<div><div>This paper introduces Coconut, a C++ tool that uses templates for defining object behaviours and validates them with typestate checking. Coconut employs the GIMPLE intermediate representation (IR) from the GCC compiler’s middle-end phase for static checks, ensuring objects follow valid state transitions as defined in typestate templates. It supports features like branching, recursion, aliasing, inheritance, and typestate visualisation. We illustrate Coconut’s application in embedded systems, validating their behaviour pre-deployment. We present an experimental study, showing that Coconut improves performance and reduces code complexity wrt the original code, highlighting the benefits of typestate-based verification.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":"250 ","pages":"Article 103398"},"PeriodicalIF":1.4,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747796","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}
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