Rapid advances in the field of artificial intelligence have made significant contributions to the automation of software development and testing stages. software created for use in various fields is tested with test s...
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The proceedings contain 12 papers. The special focus in this conference is on Fundamentals of softwareengineering. The topics include: Automated Test Generation: Taxonomy and Tool Applications;finding Universally Qua...
ISBN:
(纸本)9783031870538
The proceedings contain 12 papers. The special focus in this conference is on Fundamentals of softwareengineering. The topics include: Automated Test Generation: Taxonomy and Tool Applications;finding Universally Quantified Heap Invariants by Horn Clause Transformations;a Framework for Model-Based Specification and Verification in Feature-Oriented software Product Lines;extracting Formal Models for User’s Behaviors in Social Networks Using Automata and Machine Learning;data-Driven Shielding of Online Reinforcement Learning: A Stormwater Pond Case Study;on Explicit Solutions to Fixed-Point Equations in Propositional Dynamic Logic;on Time-Sensitive Control Closure for Secure Information Flow;automatic Generation of Loop Invariants in Dafny with Large Language Models;streamlining Parameter Tuning in Full-Body Racing Simulators with an Automated Pipeline;formally Verified Verifiable Group Generators.
In agile software development, user stories capture requirements from the user's perspective, emphasizing their needs and each feature's value. Writing concise and quality user stories is necessary for guiding...
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ISBN:
(纸本)9783031783852;9783031783869
In agile software development, user stories capture requirements from the user's perspective, emphasizing their needs and each feature's value. Writing concise and quality user stories is necessary for guiding software development. Alongside user story generation, prioritizing these requirements ensures that the most important features are developed first, maximizing project value. This study explores the use of Large Language Models (LLMs) to automate the process of user story generation, quality assessment, and prioritization. We implemented a multi-agent system using Generative Pre-trained Transformers (GPT), specifically GPT-3.5 and GPT-4o, to generate and prioritize user stories from the initial project description. Our experiments on a real-world project demonstrate that GPT-3.5 handled user story generation well, achieving a higher semantic similarity score comnpared to the GPT4o. Both models showed consistent performance in prioritizing requirements, effectively identifying the core features of the application. These early results indicate that LLMs have significant potential for automating requirements analysis, particularly generating and prioritizing user stories.
作者：
Bobade, VeerPuri, ChetanDMIHER
Faculty of Engineering And Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India DMIHER
Faculty of Engineering And Technology Department of Computer Science and Engineering Maharashtra Wardha India
Enhancing software quality and reducing testing expenses requires better software fault detection. To guarantee the dependability and usability of software, software Defect Prediction (SDP) uses machine learning appro...
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Maritime transportation business suffers from trust issues and burdensome paperwork. Blockchain-based smart contracts are a promising solution. Due to the nature of the blockchain, it is important to verify smart cont...
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ISBN:
(纸本)9783031664557;9783031664564
Maritime transportation business suffers from trust issues and burdensome paperwork. Blockchain-based smart contracts are a promising solution. Due to the nature of the blockchain, it is important to verify smart contracts before deployment, especially for its functionality and legality. In this paper, we propose a verification framework that automatically verifies the functionality and legality requirements of maritime transportation smart contracts. Smart contracts of an application, based on a set of templates, are modeled in a network of timed automata;domain-specific requirements are collected and formulated as temporal logic formulas;real-time model checking tool UPPAAL is then used to check whether these requirements are satisfied. We carry out experiments on nine real-world smart contracts to show the effectiveness and feasibility of our framework. We also compare our work with existing tools to show its effectiveness and efficiency.
Multi-Criteria Decision-Making (MCDM) plays a pivotal role in the field of computerscience and softwareengineering, offering a systematic approach to decision-making processes. The integration of various MCDM method...
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The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hard...
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The nested array results in a hole-free difference coarray (DCA) after virtualization, enhancing the utilization of virtual array elements. MUSIC algorithm is an algorithm with high estimation accuracy, but the angle ...
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To reduce distortion from the fundamental sinusoidal waveform, inverters were extended to more than two layers, which gave rise to the concept of a multilayer inverter. The need for extra switches, which increases sys...
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This work addresses a research challenge in automating the translation of natural language inputs into programming language specifications. We consider the case of bug reports, which are informally written by users, a...
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ISBN:
(纸本)9783031815720;9783031815737
This work addresses a research challenge in automating the translation of natural language inputs into programming language specifications. We consider the case of bug reports, which are informally written by users, and that must be specifying into executable test cases for reproducing the bug on the target software. software bugs are indeed largely reported in natural language by users. Yet, we lack reliable tools to automatically address reported bugs (i.e., enabling their analysis, reproduction, and bug fixing). We therefore build on the recent promises brought by ChatGPT for various tasks, including in softwareengineering, and establish the following research question: What if Conversational Artificial Intelligence (AI) models could be used to explore the semantics of bug reports as well as to automate their reproduction? We evaluate the capabilities of ChatGPT, a state-of-the-art conversational AI, i.e., chatbot, using the popular Defects4J benchmark with its associated bug reports. The results reveal that ChatGPT can generate executable test cases that could trigger 50% of the bugs reported in natural language. These results are promising not only for the research community, but also for practitioners.
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