Digitalization is perhaps the primary and most impactful development affecting industry and society. Digitalization is enabled by the digital technologies, i.e., software, data, and artificial intelligence (AI). Many companies digitalize in a slow, iterative fashion by incrementally adding new digital capabilities for individual activities and tasks in an ad-hoc and opportunistic fashion. This increases the risk of disruption. In this paper, we discuss the key challenges companies experience as part of their digital transformation and present the Radical paradigm that can play the role of a North Star for companies to align their change initiatives with. Radical is concerned with being responsive, architecture and data driven, continuous and driven by highly autonomous cross-functional teams and based on three principles, i.e., the three circles model, AI at the core and quantitative outcomes.
Automated generation of test oracles is a critical area of research in software quality assurance. One effective technique is the detection of invariants by analyzing dynamic execution data. Though a common challenge of these approaches is the detection of false-positive invariants. This paper investigates the potential of Large Language Models (LLMs) to assist in filtering these dynamically detected invariants, aiming to reduce the manual effort involved in discarding incorrect invariants. We conducted experiments using various GPT models from OpenAI, leveraging a dataset of invariants detected from the dynamic execution of REST APIs. By employing a Zero-shot Chain-of-Thought Prompting methodology, we guided the LLMs to articulate their reasoning behind their decisions. Our findings indicate that classification performance improves with detailed instructions and strategic prompt design (the best model achieving on average 80.7% accuracy), with some performance differences between different invariant types.
Through practices and tools that track and document the origins, evolution, and changes of software components, software development and provenance are intricately connected. The importance of provenance in the digital age has led to significant interest in using blockchain technology to store provenance data in a tamper-proof manner. This study presents an overview of current advancements in blockchain-based provenance storage research, addressing the benefits and challenges of various storage strategies. The storage types identified in the system literature review informed the development of a proprietary system. Due to the incorporation of numerous entities, activities, agents, and relationships, underlying documents can become substantially large. The study indicates that an effective approach to manage extensive data involves external storage while retaining only its fingerprint on the blockchain. This method ensures tamper-proof uploads and maintains traceability in the development process.
In this contribution, we introduce a Model-Based Test Design (MBTD) approach that reuses the existing diagram types of Systems Modeling Language (SysML) 1.5 to improve test design quality for larger cyber-physical systems. The approach also facilitates the traceability between requirements and test cases to measure the test coverage and supports the idea of Model-Based Systems Engineering (MBSE). We validated our approach by applying it to a real-world Systems Engineering (SE) project of a cyber-physical system.
Lean R D has been used at PUC-Rio to foster industry-academia collaboration in innovation projects across multiple sectors. This industrial experience paper describes recent experiences and evaluation results from applying Lean R D in partnership with Petrobras in the oil and gas sector and Americanas in retail. The findings highlight Lean R D's effectiveness in transforming ideas into meaningful business outcomes. Based on responses from 57 participants - including team members, managers, and sponsors - the assessment indicates that stakeholders find the structured phases of Lean R D well-suited to innovation projects and endorse the approach. Although acknowledging that successful collaboration relies on various factors, this industrial experience positions Lean R D as a promising framework for industry-academia projects focused on achieving rapid, impactful results for industry partners.
