IEEE Software

Achieving automatic, valid, verified answers from generative AI models, is a goal that many of us are working on. We must find a way to make sure we have reliable and trustworthy results despite numerous challenges in the field.

The emergence of quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. However, realizing this promise in industrial applications is far from being practical today. In this ...

In this final issue of the year, we mark a milestone: IEEE Software’s 40th anniversary—a ruby jubilee! While this column still awaits the silver achievement badge (25 years)—Suzanne Robertsson founded it in the early millennium—I’m pleased to present a ...

Trust is central to how developers engage with AI. In this article, we discuss what we learned from developers about their level of trust in AI powered developer tooling, and how we translated those findings into product design recommendations to support ...

Code refactoring is an essential part of software development, because it reduces technical debt, enhances long-term code sustainability, and enables the implementation of functionality that might have been incompatible with an original design. IDEs ...

This special issue about Generative AI (GAI) for software engineering refers to applying generative models and algorithms in software development, testing, maintenance and evolution. This special issue features five articles where you will see some ...

In this study, we investigate what has been discussed about generative AI in the code review context by performing a gray literature review. We analyzed 42 documents and found insights from practice and proposals of solutions using generative AI models.

This article delves into the potential opportunities and challenges that generative AI introduces to software security analysis based on its fundamentals and applications. In addition, a simple case is used to demonstrate the validity of our outlook.

High quality data is essential for designing effective software test suites. We propose three original methods for using large language models to generate representative test data, which fit to the domain of the program under test and are culturally ...

This article presents a novel framework that combines the hazard and operability analysis with generative AI to support a safety expert in identifying safety hazards, their causes and consequences, and to propose mitigation strategies.

The use of generative AI chatbots in formative assessment can effectively gauge learning progress, increase the academic performance of students compared to a traditional methodology, and raise student awareness about the tradeoffs of employing generative ...

Continuous integraton (CI) accelerators “turbo-charge” developer feedback cycles, but make mistakes. By replaying failing CI jobs, we study the trustworthiness of CI accelerators. Both studied products have limitations, but the program analysis product is ...

To address AI architecture design challenges, we present an architecture evolution of AI systems in the era of foundation models, transitioning from “foundation-model-as-a-connector” to “foundation-model-as-a-monolithic architecture.” We then identify key ...

We present a validated framework for the evaluation and comparison of three main third-generation blockchain categories, based on the three main nonfunctional aspects that discriminate their use for the design and orchestration of complex blockchain-...

AI code generation tools are reshaping the software engineering landscape. We provide recommendations for practitioners interested in these tools based on narratives we have collected regarding two AI code generation tools, GitHub Copilot and Tabnine.

We discuss similarities between interactive work in nursing and software engineering, presenting the results of an empirical study with 82 German software developers. We show that software engineering is affected by similar stress to nursing, concluding ...

Requirements engineering plays a crucial role in understanding stakeholder needs, ensuring quality attributes, and facilitating effective communication in various domains. Software testing helps ensure that software products meet requirements and quality ...

An ethical approach to AI need not be revolutionary or exceptional. We argue that it is the ethical duty of software professionals to rally against AI over-hype. As shown here, this is not hard to do. If we apply just simple empirical methods, we can ...

In Episode 626 of “Software Engineering Radio,” Ipek Ozkaya, principal researcher and technical director of the Engineering Intelligent Software Systems Group at the Software Engineering Institute, Carnegie Mellon, discusses generative AI for software ...