Geographically Distributed Sensemaking: Developing Understanding in Forum-Based Software Development Teams

Shreeve, Ben; Ralph, Paul; Sawyer, Pete; Stacey, Patrick

doi:10.1109/chase.2015.12

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…End-users also engage in sensemaking as they interact with and debug software programs in production [9] or navigate dense and changing software ecosystems [26]. Sensemaking is both an individual and collective activity [24] and in the case of distributed software teams, it can be a collaborative activity that takes place entirely virtually [22]. Essential for any SE project is the ability of the software team to appropriately identify and comprehend the complex, heterogenous contexts shaping their work [5].…”

Section: Introductionmentioning

confidence: 99%

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

Wolf

Paine

2020

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

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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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Section: Introductionmentioning

confidence: 99%

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

Wolf

Paine

2020

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

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“…A data consultant might care for increasing the fit between the available data and business needs through selection and pre-processing, but this processing might simultaneously make the data even more opaque to the remaining project members. This observation adds to SE literature on sensemaking as a necessary step to interpret the application context of the software product [80], [81], or as a prerequisite for mutual understanding in dispersed or inter-organizational teams [61], [82], [83]. This study advances this research by applying sensemaking to the context of AI application development.…”

Section: Platform-relatedmentioning

confidence: 71%

Making Sense of AI Systems Development

Dolata,

Crowston

2024

IIEEE Trans. Software Eng.

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We identify and describe episodes of sensemaking around challenges in modern Artificial-Intelligence (AI)-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI's inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.

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