8,676 research outputs found

    A meta-analysis approach to refactoring and XP

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    The mechanics of seventy-two different Java refactorings are described fully in Fowler's text. In the same text, Fowler describes seven categories of refactoring, into which each of the seventy-two refactorings can be placed. A current research problem in the refactoring and XP community is assessing the likely time and testing effort for each refactoring, since any single refactoring may use any number of other refactorings as part of its mechanics and, in turn, can be used by many other refactorings. In this paper, we draw on a dependency analysis carried out as part of our research in which we identify the 'Use' and 'Used By' relationships of refactorings in all seven categories. We offer reasons why refactorings in the 'Dealing with Generalisation' category seem to embrace two distinct refactoring sub-categories and how refactorings in the 'Moving Features between Objects' category also exhibit specific characteristics. In a wider sense, our meta-analysis provides a developer with concrete guidelines on which refactorings, due to their explicit dependencies, will prove problematic from an effort and testing perspective

    Augmenting IDEs with Runtime Information for Software Maintenance

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    Object-oriented language features such as inheritance, abstract types, late-binding, or polymorphism lead to distributed and scattered code, rendering a software system hard to understand and maintain. The integrated development environment (IDE), the primary tool used by developers to maintain software systems, usually purely operates on static source code and does not reveal dynamic relationships between distributed source artifacts, which makes it difficult for developers to understand and navigate software systems. Another shortcoming of today's IDEs is the large amount of information with which they typically overwhelm developers. Large software systems encompass several thousand source artifacts such as classes and methods. These static artifacts are presented by IDEs in views such as trees or source editors. To gain an understanding of a system, developers have to open many such views, which leads to a workspace cluttered with different windows or tabs. Navigating through the code or maintaining a working context is thus difficult for developers working on large software systems. In this dissertation we address the question how to augment IDEs with dynamic information to better navigate scattered code while at the same time not overwhelming developers with even more information in the IDE views. We claim that by first reducing the amount of information developers have to deal with, we are subsequently able to embed dynamic information in the familiar source perspectives of IDEs to better comprehend and navigate large software spaces. We propose means to reduce or mitigate the information by highlighting relevant source elements, by explicitly representing working context, and by automatically housekeeping the workspace in the IDE. We then improve navigation of scattered code by explicitly representing dynamic collaboration and software features in the static source perspectives of IDEs. We validate our claim by conducting empirical experiments with developers and by analyzing recorded development sessions

    Sensemaking Practices in the Everyday Work of AI/ML Software Engineering

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    This paper considers sensemaking as it relates to everyday software engineering (SE) work practices and draws on a multi-year ethnographic study of SE projects at a large, global technology company building digital services infused with artificial intelligence (AI) and machine learning (ML) capabilities. Our findings highlight the breadth of sensemaking practices in AI/ML projects, noting developers' efforts to make sense of AI/ML environments (e.g., algorithms/methods and libraries), of AI/ML model ecosystems (e.g., pre-trained models and "upstream"models), and of business-AI relations (e.g., how the AI/ML service relates to the domain context and business problem at hand). This paper builds on recent scholarship drawing attention to the integral role of sensemaking in everyday SE practices by empirically investigating how and in what ways AI/ML projects present software teams with emergent sensemaking requirements and opportunities
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