A. Allian, L. F. Silva, E. Oliveirajr, E. Nakagawa
{"title":"VMTools-RA: a Reference Architecture for Software Variability Tools","authors":"A. Allian, L. F. Silva, E. Oliveirajr, E. Nakagawa","doi":"10.3897/jucs.97113","DOIUrl":"https://doi.org/10.3897/jucs.97113","url":null,"abstract":"Currently, software systems must be appropriately developed to support an amount of variability for accommodating different requirements. To support such development, a diversity of tools has already been designed for variability management (i.e., identification, modeling, evaluation, and realization). However, due to this diversity, there is a lack of consensus on what in fact software variability tools are and even what functionalities they should provide. Besides that, the building of new tools is still an effort- and time-consuming task. To support their building, we present VMTools-RA, a reference architecture that encompasses knowledge and practice for developing and evolving variability tools. Designed in a systematic way, VMTools-RA was evaluated throughout: a controlled experiment with software developer practitioners; and an instantiation of the VMTools-RA architecture to implement a software variability tool, named SMartyModeling. As a result, VMTools-RA is evidenced to be feasible and it can be considered an important contribution to the software variability and developers of variability-intensive software systems community, which require specific tools developed in a faster manner with less risk, what a reference architecture could provide.","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123022523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Modeling Strategy for the Verification of Context-Oriented Chatbot Conversational Flows via Model Checking","authors":"G. Silva, G. Rodrigues, E. Canedo","doi":"10.3897/jucs.91311","DOIUrl":"https://doi.org/10.3897/jucs.91311","url":null,"abstract":"Verification of chatbot conversational flows is paramount to capturing and understanding chatbot behavior and predicting problems that would cause the entire flow to be restructured from scratch. The literature on chatbot testing is scarce, and the few works that approach this subject do not focus on verifying the communication sequences in tandem with the functional requirements of the conversational flow itself. However, covering all possible conversational flows of context-oriented chatbots through testing is not feasible in practice given the many ramifications that should be covered by test cases. Alternatively, model checking provides a model-based verification in a mathematically precise and unambiguous manner. Moreover, it can anticipate design flaws early in the software design phase that could lead to incompleteness, ambiguities, and inconsistencies. We postulate that finding design flaws in chatbot conversational flows via model checking early in the design phase may overcome quite a few verification gaps that are not feasible via current testing techniques for context-oriented chatbot conversational flows. Therefore, in this work, we propose a modeling strategy to design and verify chatbot conversational flows via the Uppaal model checking tool. Our strategy is materialized in the form of templates and a mapping of chatbot elements into Uppaal elements. To evaluate this strategy, we invited a few chatbot developers with different levels of expertise. The feedback from the participants revealed that the strategy is a great ally in the phases of conversational prototyping and design, as well as helping to refine requirements and revealing branching logic that can be reused in the implementation phase.","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115290861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
João Gabriel Lopes De Oliveira, Editorial office Pedro Moreira Menezes Da Costa, F. D. de Mello
{"title":"Knowledge Geometry in Phenomenon Perception and Artificial Intelligence","authors":"João Gabriel Lopes De Oliveira, Editorial office Pedro Moreira Menezes Da Costa, F. D. de Mello","doi":"10.3897/jucs.2020.032","DOIUrl":"https://doi.org/10.3897/jucs.2020.032","url":null,"abstract":"Artificial Intelligence (AI) pervades industry, entertainment, transportation, finance, and health. It seems to be in a kind of golden age, but today AI is based on the strength of techniques that bear little relation to the thought mechanism. Contemporary techniques of machine learning, deep learning and case-based reasoning seem to be occupied with delivering functional and optimized solutions, leaving aside the core reasons of why such solutions work. This paper, in turn, proposes a theoretical study of perception, a key issue for knowledge acquisition and intelligence construction. Its main concern is the formal representation of a perceived phenomenon by a casual observer and its relationship with machine intelligence. This work is based on recently proposed geometric theory, and represents an approach that is able to describe the inuence of scope, development paradigms, matching process and ground truth on phenomenon perception. As a result, it enumerates the perception variables and describes the implications for AI.","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116905497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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