Pub Date : 2023-11-16DOI: 10.1007/s10270-023-01138-w
Yizhak Yisrael Elboher, Elazar Cohen, Guy Katz
As neural networks are increasingly being integrated into mission-critical systems, it is becoming crucial to ensure that they meet various safety and liveness requirements. Toward, that end, numerous complete and sound verification techniques have been proposed in recent years, but these often suffer from severe scalability issues. One recently proposed approach for improving the scalability of verification techniques is to enhance them with abstraction/refinement capabilities: instead of verifying a complex and large network, abstraction allows the verifier to construct and then verify a much smaller network, and the correctness of the smaller network immediately implies the correctness of the original, larger network. One shortcoming of this scheme is that whenever the smaller network cannot be verified, the verifier must perform a refinement step, in which the size of the network being verified is increased. The verifier then starts verifying the new network from scratch—effectively “forgetting” its earlier work, in which the smaller network was verified. Here, we present an enhancement to abstraction-based neural network verification, which uses residual reasoning: a process where information acquired when verifying an abstract network is utilized in order to facilitate the verification of refined networks. At its core, the method enables the verifier to retain information about parts of the search space in which it was determined that the refined network behaves correctly, allowing the verifier to focus on areas of the search space where bugs might yet be discovered. For evaluation, we implemented our approach as an extension to the Marabou verifier and obtained highly promising results.
{"title":"On applying residual reasoning within neural network verification","authors":"Yizhak Yisrael Elboher, Elazar Cohen, Guy Katz","doi":"10.1007/s10270-023-01138-w","DOIUrl":"https://doi.org/10.1007/s10270-023-01138-w","url":null,"abstract":"<p>As neural networks are increasingly being integrated into mission-critical systems, it is becoming crucial to ensure that they meet various safety and liveness requirements. Toward, that end, numerous complete and sound verification techniques have been proposed in recent years, but these often suffer from severe scalability issues. One recently proposed approach for improving the scalability of verification techniques is to enhance them with abstraction/refinement capabilities: instead of verifying a complex and large network, abstraction allows the verifier to construct and then verify a much smaller network, and the correctness of the smaller network immediately implies the correctness of the original, larger network. One shortcoming of this scheme is that whenever the smaller network cannot be verified, the verifier must perform a refinement step, in which the size of the network being verified is increased. The verifier then starts verifying the new network from scratch—effectively “forgetting” its earlier work, in which the smaller network was verified. Here, we present an enhancement to abstraction-based neural network verification, which uses <i>residual reasoning</i>: a process where information acquired when verifying an abstract network is utilized in order to facilitate the verification of refined networks. At its core, the method enables the verifier to retain information about parts of the search space in which it was determined that the refined network behaves correctly, allowing the verifier to focus on areas of the search space where bugs might yet be discovered. For evaluation, we implemented our approach as an extension to the Marabou verifier and obtained highly promising results.</p>","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"64 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138542782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-15DOI: 10.1007/s10270-023-01136-y
Hessam Mohammadi, Wided Ghardallou, Elijah Brick, Ali Mili
Since the dawn of programming, several developments in programming language design and programming methodology have been hailed as the end of the profession of programmer; they have all proven to be exaggerated rumors, to echo the words attributed to Mark Twain. In this short paper, we ponder the question of whether the emergence of large language models finally realizes these prophecies? Also, we discuss why even if this prophecy is finally realized, it does not change the job of the researcher in programming.
{"title":"On the persistent rumors of the programmer’s imminent demise","authors":"Hessam Mohammadi, Wided Ghardallou, Elijah Brick, Ali Mili","doi":"10.1007/s10270-023-01136-y","DOIUrl":"https://doi.org/10.1007/s10270-023-01136-y","url":null,"abstract":"<p>Since the dawn of programming, several developments in programming language design and programming methodology have been hailed as the end of the profession of programmer; they have all proven to be exaggerated rumors, to echo the words attributed to Mark Twain. In this short paper, we ponder the question of whether the emergence of large language models finally realizes these prophecies? Also, we discuss why even if this prophecy is finally realized, it does not change the job of the researcher in programming.</p>","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"192 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138542781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-08DOI: 10.1007/s10270-023-01135-z
Moshe Hadad, Gal Engelberg, Pnina Soffer
{"title":"From network traffic data to business activities: a conceptualization and a recognition approach","authors":"Moshe Hadad, Gal Engelberg, Pnina Soffer","doi":"10.1007/s10270-023-01135-z","DOIUrl":"https://doi.org/10.1007/s10270-023-01135-z","url":null,"abstract":"","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"9 3‐4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135346033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-04DOI: 10.1007/s10270-023-01132-2
David Chapela-Campa, Marlon Dumas
Abstract Business process management (BPM) is a well-established discipline comprising a set of principles, methods, techniques, and tools to continuously improve the performance of business processes. Traditionally, most BPM decisions and activities are undertaken by business stakeholders based on manual data collection and analysis techniques. This is time-consuming and potentially leads to suboptimal decisions, as only a restricted subset of data and options are considered. Over the past decades, a rich set of data-driven techniques has emerged to support and automate various activities and decisions across the BPM lifecycle, particularly within the process mining field. More recently, the uptake of artificial intelligence (AI) methods for BPM has led to a range of approaches for proactive business process monitoring. Given their common data requirements and overlapping goals, process mining and AI-driven approaches to business process optimization are converging. This convergence is leading to a promising emerging concept, which we call (AI-)augmented process execution : a collection of data analytics and artificial intelligence methods for continuous and automated improvement and adaptation of business processes. This article gives an outline of research at the intersection between process mining and AI-driven process optimization, classifies the researched techniques based on their scope and objectives, and positions augmented process execution as an additional layer on top of this stack.
{"title":"From process mining to augmented process execution","authors":"David Chapela-Campa, Marlon Dumas","doi":"10.1007/s10270-023-01132-2","DOIUrl":"https://doi.org/10.1007/s10270-023-01132-2","url":null,"abstract":"Abstract Business process management (BPM) is a well-established discipline comprising a set of principles, methods, techniques, and tools to continuously improve the performance of business processes. Traditionally, most BPM decisions and activities are undertaken by business stakeholders based on manual data collection and analysis techniques. This is time-consuming and potentially leads to suboptimal decisions, as only a restricted subset of data and options are considered. Over the past decades, a rich set of data-driven techniques has emerged to support and automate various activities and decisions across the BPM lifecycle, particularly within the process mining field. More recently, the uptake of artificial intelligence (AI) methods for BPM has led to a range of approaches for proactive business process monitoring. Given their common data requirements and overlapping goals, process mining and AI-driven approaches to business process optimization are converging. This convergence is leading to a promising emerging concept, which we call (AI-)augmented process execution : a collection of data analytics and artificial intelligence methods for continuous and automated improvement and adaptation of business processes. This article gives an outline of research at the intersection between process mining and AI-driven process optimization, classifies the researched techniques based on their scope and objectives, and positions augmented process execution as an additional layer on top of this stack.","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"22 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135773569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-10-19DOI: 10.1007/s10270-023-01129-x
Pierre-Alain Yvars, Laurent Zimmer
{"title":"DEPS: a model- and property-based language for system synthesis problems","authors":"Pierre-Alain Yvars, Laurent Zimmer","doi":"10.1007/s10270-023-01129-x","DOIUrl":"https://doi.org/10.1007/s10270-023-01129-x","url":null,"abstract":"","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135779208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-10-10DOI: 10.1007/s10270-023-01128-y
Judith Michael, Dominik Bork, Manuel Wimmer, Heinrich C. Mayr
Abstract Models are the key tools humans use to manage complexity in description, development, and analysis. This applies to all scientific and engineering disciplines and in particular to the development of software and data-intensive systems. However, different methods and terminologies have become established in the individual disciplines, even in the sub-fields of Informatics, which raises the need for a comprehensive and cross-sectional analysis of the past, present, and future of modeling research. This paper aims to shed some light on how different modeling disciplines emerged and what characterizes them with a discussion of the potential toward a common modeling future. It focuses on the areas of software, data, and process modeling and reports on an analysis of the research approaches, goals, and visions pursued in each, as well as the methods used. This analysis is based on the results of a survey conducted in the communities concerned, on a bibliometric study, and on interviews with a prominent representative of each of these communities. The paper discusses the different viewpoints of the communities, their commonalities and differences, and identifies possible starting points for further collaboration. It further discusses current challenges for the communities in general and modeling as a research topic in particular and highlights visions for the future.
{"title":"Quo Vadis modeling?","authors":"Judith Michael, Dominik Bork, Manuel Wimmer, Heinrich C. Mayr","doi":"10.1007/s10270-023-01128-y","DOIUrl":"https://doi.org/10.1007/s10270-023-01128-y","url":null,"abstract":"Abstract Models are the key tools humans use to manage complexity in description, development, and analysis. This applies to all scientific and engineering disciplines and in particular to the development of software and data-intensive systems. However, different methods and terminologies have become established in the individual disciplines, even in the sub-fields of Informatics, which raises the need for a comprehensive and cross-sectional analysis of the past, present, and future of modeling research. This paper aims to shed some light on how different modeling disciplines emerged and what characterizes them with a discussion of the potential toward a common modeling future. It focuses on the areas of software, data, and process modeling and reports on an analysis of the research approaches, goals, and visions pursued in each, as well as the methods used. This analysis is based on the results of a survey conducted in the communities concerned, on a bibliometric study, and on interviews with a prominent representative of each of these communities. The paper discusses the different viewpoints of the communities, their commonalities and differences, and identifies possible starting points for further collaboration. It further discusses current challenges for the communities in general and modeling as a research topic in particular and highlights visions for the future.","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-10-04DOI: 10.1007/s10270-023-01127-z
Flávia Falcão, Lucas Lima, Augusto Sampaio, Pedro Antonino
{"title":"A formal component model for UML based on CSP aiming at compositional verification","authors":"Flávia Falcão, Lucas Lima, Augusto Sampaio, Pedro Antonino","doi":"10.1007/s10270-023-01127-z","DOIUrl":"https://doi.org/10.1007/s10270-023-01127-z","url":null,"abstract":"","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135597260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-09-29DOI: 10.1007/s10270-023-01123-3
Bentley James Oakes, Javier Troya, Jessie Galasso, Manuel Wimmer
Abstract The verification of model transformations is important for realizing robust model-driven engineering technologies and quality-assured automation. Many approaches for checking properties of model transformations have been proposed. Most of them have focused on the effective and efficient detection of property violations by contract checking. However, there remains the fault localization step between identifying a failing contract for a transformation based on verification feedback and precisely identifying the faulty rules. While there exist fault localization approaches in the model transformation verification literature, these require the creation and maintenance of test cases , which imposes an additional burden on the developer. In this paper, we combine transformation verification based on symbolic execution with spectrum-based fault localization techniques for identifying the faulty rules in DSLTrans model transformations. This fault localization approach operates on the path condition output of symbolic transformation checkers instead of requiring a set of test input models. In particular, we introduce a workflow for running the symbolic execution of a model transformation, evaluating the defined contracts for satisfaction, and computing different measures for tracking the faulty rules. We evaluate the effectiveness of spectrum-based analysis techniques for tracking faulty rules and compare our approach to previous works. We evaluate our technique by introducing known mutations into five model transformations. Our results show that the best spectrum-based analysis techniques allow for effective fault localization, showing an average EXAM score below 0.30 (less than 30% of the transformation needs to be inspected). These techniques are also able to locate the faulty rule in the top-three ranked rules in 70% of all cases. The impact of the model transformation, the type of mutation and the type of contract on the results is discussed. Finally, we also investigate the cases where the technique does not work properly, including discussion of a potential pre-check to estimate the prospects of the technique for a certain transformation.
{"title":"Fault localization in DSLTrans model transformations by combining symbolic execution and spectrum-based analysis","authors":"Bentley James Oakes, Javier Troya, Jessie Galasso, Manuel Wimmer","doi":"10.1007/s10270-023-01123-3","DOIUrl":"https://doi.org/10.1007/s10270-023-01123-3","url":null,"abstract":"Abstract The verification of model transformations is important for realizing robust model-driven engineering technologies and quality-assured automation. Many approaches for checking properties of model transformations have been proposed. Most of them have focused on the effective and efficient detection of property violations by contract checking. However, there remains the fault localization step between identifying a failing contract for a transformation based on verification feedback and precisely identifying the faulty rules. While there exist fault localization approaches in the model transformation verification literature, these require the creation and maintenance of test cases , which imposes an additional burden on the developer. In this paper, we combine transformation verification based on symbolic execution with spectrum-based fault localization techniques for identifying the faulty rules in DSLTrans model transformations. This fault localization approach operates on the path condition output of symbolic transformation checkers instead of requiring a set of test input models. In particular, we introduce a workflow for running the symbolic execution of a model transformation, evaluating the defined contracts for satisfaction, and computing different measures for tracking the faulty rules. We evaluate the effectiveness of spectrum-based analysis techniques for tracking faulty rules and compare our approach to previous works. We evaluate our technique by introducing known mutations into five model transformations. Our results show that the best spectrum-based analysis techniques allow for effective fault localization, showing an average EXAM score below 0.30 (less than 30% of the transformation needs to be inspected). These techniques are also able to locate the faulty rule in the top-three ranked rules in 70% of all cases. The impact of the model transformation, the type of mutation and the type of contract on the results is discussed. Finally, we also investigate the cases where the technique does not work properly, including discussion of a potential pre-check to estimate the prospects of the technique for a certain transformation.","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135194014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-09-16DOI: 10.1007/s10270-023-01126-0
Benoit Combemale, Jeff Gray, Bernhard Rumpe
{"title":"Large language models as an “operating” system for software and systems modeling","authors":"Benoit Combemale, Jeff Gray, Bernhard Rumpe","doi":"10.1007/s10270-023-01126-0","DOIUrl":"https://doi.org/10.1007/s10270-023-01126-0","url":null,"abstract":"","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135306573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-08-28DOI: 10.1007/s10270-023-01121-5
B. Archibald, M. Calder, Michele Sevegnani, Mengwei Xu
{"title":"Quantitative modelling and analysis of BDI agents","authors":"B. Archibald, M. Calder, Michele Sevegnani, Mengwei Xu","doi":"10.1007/s10270-023-01121-5","DOIUrl":"https://doi.org/10.1007/s10270-023-01121-5","url":null,"abstract":"","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"44 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80510739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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