The softwaredevelopment Life Cycle (SDLC) comprises multiple phases, each requiring Subject Matter Experts (SMEs) with phase-specific skills. The efficacy and quality of deliverables of each phase are skill dependent...
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ISBN:
(纸本)9798400717673
The softwaredevelopment Life Cycle (SDLC) comprises multiple phases, each requiring Subject Matter Experts (SMEs) with phase-specific skills. The efficacy and quality of deliverables of each phase are skill dependent. In recent times, Generative AI techniques, including Large-scale Language Models (LLMs) like GPT, have become significant players in software engineering. These models, trained on extensive text data, can offer valuable contributions to softwaredevelopment. Interacting with LLMs involves feeding prompts with the context information and guiding the generation of textual responses. The quality of the response is dependent on the quality of the prompt given. This paper proposes a systematic prompting approach based on meta-model concepts for SDLC phases. The approach is validated using ChatGPT for small but complex business application development. We share the approach and our experience, learnings, benefits obtained, and the challenges encountered while applying the approach using ChatGPT. Our experience indicates that Generative AI techniques, such as ChatGPT, have the potential to reduce the skills barrier and accelerate softwaredevelopment substantially.
This paper presents a knowledge-driven approach for automated synthesis of controllers in three different use-cases. The approach addresses the engineering challenge posed by Industrie 4.0, which requires fast, reliab...
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This paper presents a knowledge-driven approach for automated synthesis of controllers in three different use-cases. The approach addresses the engineering challenge posed by Industrie 4.0, which requires fast, reliable, and flexible integration of multiple heterogeneous hardware and software components. Manual design approaches are not scalable for large systems due to their complexity. The proposed approach captures resource-capability knowledge and uses a reasoning-based synthesis mechanism to compose a controller design for a plant goal. The approach uses domain-specific languages (DSLs) to describe the components, their interfaces, and capabilities. The generated control designs are executable codes that implement the control strategy. The proposed approach reduces the average engineering time by 70% and generates on an average 60% of the executable code in each use-case. The approach uses a knowledge repository to store resource-capability knowledge and enables rapid prototyping and iterative design. The proposed approach provides a promising solution to automate the synthesis of controllers in different use-cases with multiple heterogeneous hardware and software components satisfaction.
Security of cryptographic protocols can be analysed by creating a model in a formal language and verifying the model in a tool. All such tools focus on the last part of the analysis, verification, and the interpretati...
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ISBN:
(纸本)9781450392877
Security of cryptographic protocols can be analysed by creating a model in a formal language and verifying the model in a tool. All such tools focus on the last part of the analysis, verification, and the interpretation of the specification is only explained in papers. Rather, we focus on the interpretation and modelling part by presenting a tool to aid the cryptographer throughout the process and automatically generating code in a target language. We adopt a data-centric approach where the protocol design is stored in a structured way rather than as textual specifications. Previous work shows how this approach facilitates the interpretation to a single language (for Tamarin) which required aftermath modifications. By improving the expressiveness of the specification data structure we extend the tool to export to an additional formal language, ProVerif, as well as a C++ fully running implementation. Furthermore, we extend the plugins to verify correctness in ProVerif and executabiity lemmas in Tamarin. In this paper we model the Diffie-Hellman key exchange, which is traditionally used as a case study;a demo is also provided for other commonly studied protocols, Needham-Schroeder and Needham-Schroeder-Lowe.
Every product development starts with idea conceptualization and design. The ideators and designers first choice is sketching the idea to conceptualize the product. After a few iterations of product design, mockups/pr...
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ISBN:
(纸本)9783030304652;9783030304645
Every product development starts with idea conceptualization and design. The ideators and designers first choice is sketching the idea to conceptualize the product. After a few iterations of product design, mockups/prototypes are passed to developers who have the job of converting these designs into a functional product by grasping the design concepts and using their coding skills. Utilizing the advancements in deep learning and computer vision technologies, gap between designers and developers is bridged by simplifying the product development process. This will also help people with little or no knowledge of web development to bring their design ideas into reality. The service will consist of two stages firstly accepting images of web mockups including hand-drawn sketches and identifying the various HTML components in the sketch using Convolutional Neural Networks and tag them appropriately, secondly, the identified components will be converted to structured JSON files which will be converted to code based on HTML5, Flexbox and Bootstrap.
software systems are often released without formal specifications. To deal with the problem of lack of and outdated specifications, rule-based specification mining approaches have been proposed. These approaches analy...
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software systems are often released without formal specifications. To deal with the problem of lack of and outdated specifications, rule-based specification mining approaches have been proposed. These approaches analyze execution traces of a system to infer the rules that characterize the protocols, typically of a library, that its clients must obey. Rule-based specification mining approaches work by exploring the search space of all possible rules and use interestingness measures to differentiate specifications from false positives. Previous rule-based specification mining approaches often rely on one or two interestingness measures, while the potential benefit of combining multiple available interestingness measures is not yet investigated. In this work, we propose a learning to rank based approach that automatically learns a good combination of 38 interestingness measures. Our experiments show that the learning to rank based approach outperforms the best performing approach leveraging single interestingness measure by up to 66%.
The problem of creating a software platform for the automated iterative development of applied knowledge-based systems is considered in the article. Well-known component-based software engineering methodology is utili...
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ISBN:
(纸本)9789532330953
The problem of creating a software platform for the automated iterative development of applied knowledge-based systems is considered in the article. Well-known component-based software engineering methodology is utilized as a general approach. The proposed architecture of the software platform includes the management subsystem and a set of problem-oriented components that, in addition to implementing the main functionality, should support the unified platform component interface. The management sub-system provides the ability to interactively define functions of the application based on the combination of the platform components methods via visual programming technique. The list of frequently used functions and features of knowledge-based systems and corresponding platform components is suggested in the article. The data control component implements methods for interacting with a data source. The next component provides the ability to create subject domain model in the ontology form. The rule-based reasoning component provides the ability to create a knowledge base on the top of the obtained domain ontology. The data representation component supplies an automatic creation of elements and forms of the user interface. As an illustrative example, the development process of the knowledge-based system for decision support in the infrastructure logistics domain is presented.
Artificial intelligence and recent advances in deep learning architectures, including transformer networks and large language models, change the way people think and act to solve problems. software engineering, as an ...
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Artificial intelligence and recent advances in deep learning architectures, including transformer networks and large language models, change the way people think and act to solve problems. software engineering, as an increasingly complex process to design, develop, test, deploy, and maintain large-scale software systems for solving real-world challenges, is profoundly affected by many revolutionary artificial intelligence tools in general, and machine learning in particular. In this roadmap for artificial intelligence in software engineering, we highlight the recent deep impact of artificial intelligence on software engineering by discussing successful stories of applications of artificial intelligence to classic and new softwaredevelopment challenges. We identify the new challenges that the software engineering community has to address in the coming years to successfully apply artificial intelligence in software engineering, and we share our research roadmap towards the effective use of artificial intelligence in the software engineering profession, while still protecting fundamental human *** spotlight three main areas that challenge the research in software engineering: the use of generative artificial intelligence and large language models for engineering large software systems, the need of large and unbiased datasets and benchmarks for training and evaluating deep learning and large language models for software engineering, and the need of a new code of digital ethics to apply artificial intelligence in software engineering.
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