Pub Date : 2024-10-17DOI: 10.1016/j.scico.2024.103220
Runtime Verification is a lightweight formal verification technique used to verify whether a system behaves as expected at runtime. Expected behaviour is typically formally specified using properties, which are used to automatically synthesise monitors. Properties that can be verified at runtime by a monitor are called monitorable, while those that cannot are termed non-monitorable. In this paper, we revisit the notion of monitorability and demonstrate how non-monitorable properties can still be used to generate partial monitors. We tackle this from two different perspectives: (i) by recognising that a monitor can give up on monitoring the property under analysis if it recognises that the monitoring will never conclude the satisfaction or violation of the property; (ii) by recognising that a monitor can give up on events that are not necessary for successful monitoring of the property under analysis. By considering these two aspects, we present how to achieve partial monitoring of Linear Temporal Logic properties by building upon the standard monitor construction. Finally, we present a prototype implementation of our approach and its application to a remote inspection case study, as well as a set of evaluation experiments to stress test our approach using synthetic properties.
{"title":"Towards partial monitoring: Never too early to give in","authors":"","doi":"10.1016/j.scico.2024.103220","DOIUrl":"10.1016/j.scico.2024.103220","url":null,"abstract":"<div><div>Runtime Verification is a lightweight formal verification technique used to verify whether a system behaves as expected at runtime. Expected behaviour is typically formally specified using properties, which are used to automatically synthesise monitors. Properties that can be verified at runtime by a monitor are called <em>monitorable</em>, while those that cannot are termed <em>non-monitorable</em>. In this paper, we revisit the notion of monitorability and demonstrate how <em>non-monitorable</em> properties can still be used to generate <em>partial</em> monitors. We tackle this from two different perspectives: (i) by recognising that a monitor can give up on monitoring the property under analysis if it recognises that the monitoring will never conclude the satisfaction or violation of the property; (ii) by recognising that a monitor can give up on events that are not necessary for successful monitoring of the property under analysis. By considering these two aspects, we present how to achieve partial monitoring of Linear Temporal Logic properties by building upon the standard monitor construction. Finally, we present a prototype implementation of our approach and its application to a remote inspection case study, as well as a set of evaluation experiments to stress test our approach using synthetic properties.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1016/j.scico.2024.103217
As in the classical computing realm, quantum programming languages in quantum computing allow one to instruct a quantum computer to perform certain tasks. In the last 25 years, many imperative, functional, and multi-paradigm quantum programming languages with different features and goals have been developed. However, to the best of our knowledge, no study has investigated who uses quantum languages, how practitioners learn a quantum language, how experience are practitioners with quantum languages, what is the most used quantum languages, in which context practitioners use quantum languages, what are the challenges faced by quantum practitioners while using quantum languages, are program written with quantum languages tested, and what are quantum practitioners' perspectives on the variety of quantum languages and the potential need for new languages. In this paper, we first conduct a systematic survey to find and collect all quantum languages proposed in the literature and/or by organizations. Secondly, we identify and describe 37 quantum languages. Thirdly, we survey 251 quantum practitioners to answer several research questions about their quantum language usage. Fourthly, we conclude that (i) 58.2% of all practitioners are 25–44 years old, 63.0% have a master's or doctoral degree, and 86.2% have more than five years of experience using classical languages. (ii) 60.6% of practitioners learn quantum languages from the official documentation. (iii) Only 16.3% of practitioners have more than five years of experience with quantum languages. (iv) Qiskit (Python) is the most used quantum language, followed by Cirq (Python) and QDK (Q#). (v) 42.8% use quantum languages for research. (vi) Lack of documentation and usage examples are practitioners' most challenging issues. Practitioners prefer open-source quantum languages with an easy-to-learn syntax (e.g., based on an existing classical language), available documentation and examples, and an active community. (vii) 76.4% of all participants test their quantum programs, and 42.6% test them automatically. (viii) A standard quantum language, perhaps high-level language, for quantum computation could accelerate the development of quantum programs. Finally, we present a set of suggestions for developers and researchers on the development of new quantum languages or enhancement of existing ones.
{"title":"An exploratory study on the usage of quantum programming languages","authors":"","doi":"10.1016/j.scico.2024.103217","DOIUrl":"10.1016/j.scico.2024.103217","url":null,"abstract":"<div><div>As in the classical computing realm, quantum programming languages in quantum computing allow one to instruct a quantum computer to perform certain tasks. In the last 25 years, many imperative, functional, and multi-paradigm quantum programming languages with different features and goals have been developed. However, to the best of our knowledge, no study has investigated who uses quantum languages, how practitioners learn a quantum language, how experience are practitioners with quantum languages, what is the most used quantum languages, in which context practitioners use quantum languages, what are the challenges faced by quantum practitioners while using quantum languages, are program written with quantum languages tested, and what are quantum practitioners' perspectives on the variety of quantum languages and the potential need for new languages. In this paper, we first conduct a systematic survey to find and collect all quantum languages proposed in the literature and/or by organizations. Secondly, we identify and describe 37 quantum languages. Thirdly, we survey 251 quantum practitioners to answer several research questions about their quantum language usage. Fourthly, we conclude that (i) 58.2% of all practitioners are 25–44 years old, 63.0% have a master's or doctoral degree, and 86.2% have more than five years of experience using classical languages. (ii) 60.6% of practitioners learn quantum languages from the official documentation. (iii) Only 16.3% of practitioners have more than five years of experience with quantum languages. (iv) Qiskit (Python) is the most used quantum language, followed by Cirq (Python) and QDK (Q#). (v) 42.8% use quantum languages for research. (vi) Lack of documentation and usage examples are practitioners' most challenging issues. Practitioners prefer open-source quantum languages with an easy-to-learn syntax (e.g., based on an existing classical language), available documentation and examples, and an active community. (vii) 76.4% of all participants test their quantum programs, and 42.6% test them automatically. (viii) A standard quantum language, perhaps high-level language, for quantum computation could accelerate the development of quantum programs. Finally, we present a set of suggestions for developers and researchers on the development of new quantum languages or enhancement of existing ones.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424074","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 : 2024-10-03DOI: 10.1016/j.scico.2024.103215
We present JoT, a testing framework for Microservice Architectures (MSAs) based on technology agnosticism, a core principle of microservices. The main advantage of JoT is that it reduces the amount of work for a) testing for MSAs whose services use different technology stacks, b) writing tests that involve multiple services, and c) reusing tests of the same MSA under different deployment configurations or after changing some of its components. In JoT, tests are orchestrators that can both consume or offer operations from/to the MSA under test. The language for writing JoT tests is Jolie, which provides constructs that support technology agnosticism and the definition of terse test behaviours.
{"title":"JoT: A Jolie framework for testing microservices","authors":"","doi":"10.1016/j.scico.2024.103215","DOIUrl":"10.1016/j.scico.2024.103215","url":null,"abstract":"<div><div>We present JoT, a testing framework for Microservice Architectures (MSAs) based on technology agnosticism, a core principle of microservices. The main advantage of JoT is that it reduces the amount of work for a) testing for MSAs whose services use different technology stacks, b) writing tests that involve multiple services, and c) reusing tests of the same MSA under different deployment configurations or after changing some of its components. In JoT, tests are orchestrators that can both consume or offer operations from/to the MSA under test. The language for writing JoT tests is Jolie, which provides constructs that support technology agnosticism and the definition of terse test behaviours.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441135","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 : 2024-10-03DOI: 10.1016/j.scico.2024.103218
Context
Stakeholders constantly make assumptions in the development of deep learning (DL) frameworks. These assumptions are related to various types of software artifacts (e.g., requirements, design decisions, and technical debt) and can turn out to be invalid, leading to system failures. Existing approaches and tools for assumption management usually depend on manual identification of assumptions. However, assumptions are scattered in various sources (e.g., code comments, commits, pull requests, and issues) of DL framework development, and manually identifying assumptions has high costs (e.g., time and resources).
Objective
The objective of the study is to evaluate different classification models for the purpose of identification with respect to assumptions from the point of view of developers and users in the context of DL framework projects (i.e., issues, pull requests, and commits) on GitHub.
Method
First, we constructed a new and largest dataset (i.e., the AssuEval dataset) of assumptions collected from the TensorFlow and Keras repositories on GitHub. Then we explored the performance of seven non-transformers based models (e.g., Support Vector Machine, Classification and Regression Trees), the ALBERT model, and three decoder-only models (i.e., ChatGPT, Claude, and Gemini) for identifying assumptions on the AssuEval dataset.
Results
The study results show that ALBERT achieves the best performance (f1-score: 0.9584) for identifying assumptions on the AssuEval dataset, which is much better than the other models (the 2nd best f1-score is 0.8858, achieved by the Claude 3.5 Sonnet model). Though ChatGPT, Claude, and Gemini are popular models, we do not recommend using them to identify assumptions in DL framework development because of their low performance. Fine-tuning ChatGPT, Claude, Gemini, or other language models (e.g., Llama3, Falcon, and BLOOM) specifically for assumptions might improve their performance for assumption identification.
Conclusions
This study provides researchers with the largest dataset of assumptions for further research (e.g., assumption classification, evaluation, and reasoning) and helps researchers and practitioners better understand assumptions and how to manage them in their projects (e.g., selection of classification models for identifying assumptions).
{"title":"An exploratory study on automatic identification of assumptions in the development of deep learning frameworks","authors":"","doi":"10.1016/j.scico.2024.103218","DOIUrl":"10.1016/j.scico.2024.103218","url":null,"abstract":"<div><h3>Context</h3><div>Stakeholders constantly make assumptions in the development of deep learning (DL) frameworks. These assumptions are related to various types of software artifacts (e.g., requirements, design decisions, and technical debt) and can turn out to be invalid, leading to system failures. Existing approaches and tools for assumption management usually depend on manual identification of assumptions. However, assumptions are scattered in various sources (e.g., code comments, commits, pull requests, and issues) of DL framework development, and manually identifying assumptions has high costs (e.g., time and resources).</div></div><div><h3>Objective</h3><div>The objective of the study is to evaluate different classification models for the purpose of identification with respect to assumptions from the point of view of developers and users in the context of DL framework projects (i.e., issues, pull requests, and commits) on GitHub.</div></div><div><h3>Method</h3><div>First, we constructed a new and largest dataset (i.e., the AssuEval dataset) of assumptions collected from the TensorFlow and Keras repositories on GitHub. Then we explored the performance of seven non-transformers based models (e.g., Support Vector Machine, Classification and Regression Trees), the ALBERT model, and three decoder-only models (i.e., ChatGPT, Claude, and Gemini) for identifying assumptions on the AssuEval dataset.</div></div><div><h3>Results</h3><div>The study results show that ALBERT achieves the best performance (f1-score: 0.9584) for identifying assumptions on the AssuEval dataset, which is much better than the other models (the 2nd best f1-score is 0.8858, achieved by the Claude 3.5 Sonnet model). Though ChatGPT, Claude, and Gemini are popular models, we do not recommend using them to identify assumptions in DL framework development because of their low performance. Fine-tuning ChatGPT, Claude, Gemini, or other language models (e.g., Llama3, Falcon, and BLOOM) specifically for assumptions might improve their performance for assumption identification.</div></div><div><h3>Conclusions</h3><div>This study provides researchers with the largest dataset of assumptions for further research (e.g., assumption classification, evaluation, and reasoning) and helps researchers and practitioners better understand assumptions and how to manage them in their projects (e.g., selection of classification models for identifying assumptions).</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424071","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 : 2024-09-30DOI: 10.1016/j.scico.2024.103216
Identifying co-change candidates when making changes to a particular program entity is an important software engineering task which is known as change impact analysis. A number of techniques for detecting co-change candidates currently exist. Most of these techniques work considering file level, class level, or method level granularity. FLeCCS is a recently introduced technique that suggests co-change candidates considering even fragment level granularity. FLeCCS ranks its co-change candidates considering file proximity. However, programmer sensitivity might be an important factor while ranking co-change candidates. Intuitively, it is more comfortable for a programmer to change her own code rather than changing someone else's code. Considering this fact, we investigate ranking co-change candidates suggested by FLeCCS considering programmer sensitivity.
In our research, we have two main contributions: (1) proposing a composite ranking mechanism considering three things - file proximity, code similarity, and programmer sensitivity and (2) an empirical evaluation of our proposed mechanism by comparing it with file proximity ranking and similarity extent ranking mechanisms on thousands of revisions of five subject systems. We compare the ranking mechanisms in ranking the co-change candidates suggested by FLeCCS. According to our investigation incorporating four measures (Precision At K, Recall At K, Mean Average Precision and Mean Reciprocal Rank), composite ranking mechanism significantly outperforms the other two existing ranking mechanisms. Our findings are supported by statistical significance tests. We believe that our Proposed Composite Ranking mechanism is not only applicable to FLeCCS but also to other co-change suggestion techniques.
{"title":"Ranking co-change candidates suggested by FLeCCS using programmer sensitivity","authors":"","doi":"10.1016/j.scico.2024.103216","DOIUrl":"10.1016/j.scico.2024.103216","url":null,"abstract":"<div><div>Identifying co-change candidates when making changes to a particular program entity is an important software engineering task which is known as change impact analysis. A number of techniques for detecting co-change candidates currently exist. Most of these techniques work considering file level, class level, or method level granularity. FLeCCS is a recently introduced technique that suggests co-change candidates considering even fragment level granularity. FLeCCS ranks its co-change candidates considering file proximity. However, programmer sensitivity might be an important factor while ranking co-change candidates. Intuitively, it is more comfortable for a programmer to change her own code rather than changing someone else's code. Considering this fact, we investigate ranking co-change candidates suggested by FLeCCS considering programmer sensitivity.</div><div>In our research, we have two main contributions: (1) proposing a composite ranking mechanism considering three things - file proximity, code similarity, and programmer sensitivity and (2) an empirical evaluation of our proposed mechanism by comparing it with file proximity ranking and similarity extent ranking mechanisms on thousands of revisions of five subject systems. We compare the ranking mechanisms in ranking the co-change candidates suggested by FLeCCS. According to our investigation incorporating four measures (Precision At K, Recall At K, Mean Average Precision and Mean Reciprocal Rank), composite ranking mechanism significantly outperforms the other two existing ranking mechanisms. Our findings are supported by statistical significance tests. We believe that our Proposed Composite Ranking mechanism is not only applicable to FLeCCS but also to other co-change suggestion techniques.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358918","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 : 2024-09-26DOI: 10.1016/j.scico.2024.103214
FM Fact Label is a tool for visualizing the characterizations of feature models based on their metadata, structural measures, and analytical metrics. Although there are various metrics available to characterize feature models, there is no standard method to visualize and identify unique properties of feature models. Unlike existing tools, FM Fact Label provides a standalone web-based platform for configurable and interactive visualization, enabling export to various formats. This contribution is significant because it supports the Universal Variability Language (UVL) and enhances the UVL ecosystem by offering a common representation of the results of existing analysis tools.
FM Fact Label 是一种基于元数据、结构度量和分析度量对特征模型特征进行可视化的工具。虽然有各种指标可用于描述特征模型，但没有标准方法来可视化和识别特征模型的独特属性。与现有工具不同，FM Fact Label 提供了一个独立的基于网络的平台，用于可配置的交互式可视化，并可导出为各种格式。这一贡献意义重大，因为它支持通用变异性语言（UVL），并通过提供现有分析工具结果的通用表示方法来增强 UVL 生态系统。
{"title":"FM fact label","authors":"","doi":"10.1016/j.scico.2024.103214","DOIUrl":"10.1016/j.scico.2024.103214","url":null,"abstract":"<div><div><em>FM Fact Label</em> is a tool for visualizing the characterizations of feature models based on their metadata, structural measures, and analytical metrics. Although there are various metrics available to characterize feature models, there is no standard method to visualize and identify unique properties of feature models. Unlike existing tools, <em>FM Fact Label</em> provides a standalone web-based platform for configurable and interactive visualization, enabling export to various formats. This contribution is significant because it supports the Universal Variability Language (UVL) and enhances the UVL ecosystem by offering a common representation of the results of existing analysis tools.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.scico.2024.103211
This Systematic Literature Review (SLR) provides a thorough analysis of the most pertinent research with regard to testing Blockchain oriented Software (BoS). Indeed, it is highly required to test and check the correctness of such kind of software systems before their deployment since they are immutable and cannot be modified once deployed. All the selected primary studies, published between 2017 and October 2022, make use of dynamic testing techniques in order to ensure the trustworthiness and the quality of BoS and to check functional and non-functional aspects such as security and performance. First of all, we study 53 research papers from various electronic databases and classify them according to several factors including (i) the accessibility of smart contract code, (ii) the adopted testing techniques and (iii) the supported vulnerabilities. Then, we emphasize works' benefits and drawbacks while providing a summary about the work done on test generation and selection in this field. At the end, we identify research challenges and potential research opportunities with the aim of giving research lines not yet explored.
{"title":"A systematic literature review on dynamic testing of blockchain oriented software","authors":"","doi":"10.1016/j.scico.2024.103211","DOIUrl":"10.1016/j.scico.2024.103211","url":null,"abstract":"<div><div>This Systematic Literature Review (SLR) provides a thorough analysis of the most pertinent research with regard to testing Blockchain oriented Software (BoS). Indeed, it is highly required to test and check the correctness of such kind of software systems before their deployment since they are immutable and cannot be modified once deployed. All the selected primary studies, published between 2017 and October 2022, make use of dynamic testing techniques in order to ensure the trustworthiness and the quality of BoS and to check functional and non-functional aspects such as security and performance. First of all, we study <strong>53</strong> research papers from various electronic databases and classify them according to several factors including (i) the accessibility of smart contract code, (ii) the adopted testing techniques and (iii) the supported vulnerabilities. Then, we emphasize works' benefits and drawbacks while providing a summary about the work done on test generation and selection in this field. At the end, we identify research challenges and potential research opportunities with the aim of giving research lines not yet explored.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323997","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 : 2024-09-25DOI: 10.1016/j.scico.2024.103213
This paper introduces two stochastic variants of rectangular automata. First, rectangular automata with random events (RAE) are introduced, which semantically embed random events. Second, in rectangular automata with random clocks (RAC), the dynamics of random events are explicitly modeled as stopwatches which are called random clocks. We show that RAE can be transformed into RAC maintaining time- and jump-bounded reachability. Both modeling variants incorporate time-induced nondeterminism on discrete behavior and nondeterminism in the dynamic behavior. The difference between RAE and RAC lies in the modeling of the random events: while RAE semantically ensure that random events are correctly handled via stochastic guards, in RAC it is the responsibility of the modeler to ensure, e.g., that random clocks are enabled and disabled such that the resulting random delay correctly models the desired random event. However, the advantage of RAC is that existing methods for nonstochastic rectangular automata can directly be applied to RAC to compute the reachable state space. We then propose an algorithm to maximize reachability probabilities for RAC with history-dependent prophetic scheduling. Specifically, we use a backward refinement approach to identify the maximum prophetic scheduler and prove the correctness of the proposed method. The feasibility of the presented approach is illustrated on a scalable model and the results computed with our tool RealySt are validated against the tool ProHVer.
{"title":"Maximizing reachability probabilities in rectangular automata with random events","authors":"","doi":"10.1016/j.scico.2024.103213","DOIUrl":"10.1016/j.scico.2024.103213","url":null,"abstract":"<div><div>This paper introduces two stochastic variants of rectangular automata. First, <em>rectangular automata with random events (RAE)</em> are introduced, which semantically embed random events. Second, in <em>rectangular automata with random clocks (RAC)</em>, the dynamics of random events are explicitly modeled as stopwatches which are called <em>random clocks</em>. We show that RAE can be transformed into RAC maintaining time- and jump-bounded reachability. Both modeling variants incorporate time-induced nondeterminism on discrete behavior and nondeterminism in the dynamic behavior. The difference between RAE and RAC lies in the modeling of the random events: while RAE semantically ensure that random events are correctly handled via stochastic guards, in RAC it is the responsibility of the modeler to ensure, e.g., that random clocks are enabled and disabled such that the resulting random delay correctly models the desired random event. However, the advantage of RAC is that existing methods for nonstochastic rectangular automata can directly be applied to RAC to compute the reachable state space. We then propose an algorithm to maximize reachability probabilities for RAC with history-dependent prophetic scheduling. Specifically, we use a backward refinement approach to identify the maximum prophetic scheduler and prove the correctness of the proposed method. The feasibility of the presented approach is illustrated on a scalable model and the results computed with our tool <span>RealySt</span> are validated against the tool <span>ProHVer</span>.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1016/j.scico.2024.103209
Testing of highly configurable systems is challenging due to an immense number of configurations and is usually performed on a small sample set. Its quality is often measured with t-wise coverage. We propose a tool for sampling highly configurable systems with thousands of features capable of achieving high t-wise coverage. The effectiveness and scalability of the tool are based on two novel techniques: the adaptive weighted sampling and the approximation techniques for the t-wise coverage computation. can easily handle sampling and computation for 6-wise coverage. The latest version supports multi-valued features.
由于配置数量巨大，对高度可配置系统进行测试极具挑战性，而且通常是在较小的样本集上进行测试。其质量通常用 t 值覆盖率来衡量。我们提出了一种工具 Baital，用于对具有数千种特征的高可配置系统进行采样，从而实现高 t-wise 覆盖率。该工具的有效性和可扩展性基于两种新技术：自适应加权采样和 t 值覆盖率计算近似技术。Baital 可以轻松处理 6 维覆盖率的采样和计算。最新版本支持多值特征。
{"title":"Baital: Sampling configurable systems with high t-wise coverage","authors":"","doi":"10.1016/j.scico.2024.103209","DOIUrl":"10.1016/j.scico.2024.103209","url":null,"abstract":"<div><div>Testing of highly configurable systems is challenging due to an immense number of configurations and is usually performed on a small sample set. Its quality is often measured with <em>t</em>-wise coverage. We propose a tool <span><math><mi>Baital</mi></math></span> for sampling highly configurable systems with thousands of features capable of achieving high <em>t</em>-wise coverage. The effectiveness and scalability of the tool are based on two novel techniques: the adaptive weighted sampling and the approximation techniques for the <em>t</em>-wise coverage computation. <span><math><mi>Baital</mi></math></span> can easily handle sampling and computation for 6-wise coverage. The latest version supports multi-valued features.</div></div>","PeriodicalId":49561,"journal":{"name":"Science of Computer Programming","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319085","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 : 2024-09-23DOI: 10.1016/j.scico.2024.103212
As the complexity and criticality of software increase every year, so does the importance of runtime monitoring. Third-party and best-effort monitoring are especially valuable, yet under-explored areas of runtime monitoring. In this context, third-party monitoring means monitoring with a limited knowledge of the monitored software (as it has been developed by a third party). Best-effort monitoring keeps pace with the monitored software at the cost of possibly imprecise verdicts when keeping up with the monitored software would not be feasible. Most existing monitoring frameworks do not support the combination of third-party and best-effort monitoring because they either require the full access to the monitored code or the ability to process all observable events, or both.
We present a middleware framework, Vamos, for the runtime monitoring of software. Vamos is explicitly designed to support third-party and best-effort scenarios. The design goals of Vamos are (i) efficiency (tracing events with low overhead), (ii) flexibility (the ability to monitor a variety of different event channels, and to connect to a wide range of monitors), and (iii) ease-of-use. To achieve its goals, Vamos combines aspects of event broker and event recognition systems with aspects of stream processing systems.
We implemented a prototype toolchain for Vamos and conducted a set of experiments demonstrating the usability of the scheme. The results indicate that Vamos enables writing useful yet efficient monitors, and simplifies key aspects of setting up a monitoring system from scratch.
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