In the early 90s, Ward Cunningham coined “Technical Debt” as a metaphor to justify feature-neutral redesign work on a finance-related product. While that usage has some persuasive power, if we focus in on the details of the metaphor we see that the financial analogy is problematic at best. Other metaphors have been produced to address the resulting managerial and organizational mismatch. Maybe pollution is a better metaphor? Or maybe it’s a matter of portraying it as cleaning the kitchen as part of meal prep. I’ll walk through a few metaphorical models, highlighting where they have good explanatory power. But in the end, we may want to consider whether a metaphorical name for this phenomenon is helpful at all.
Titus Winters (@tituswinters) is a principal Engineer at Google, where he has worked since 2010. He is the library lead for Google’s C++ codebase: 250 million lines of code that will be edited by 12K distinct engineers in a month. That unique scale and perspective has informed all of his thinking on the care and feeding of software systems, especially shown in the book “Software Engineering at Google” (aka “The Flamingo Book”). His recent areas of interest include technical debt, software engineering education, and effective software testing.
AI-based systems are becoming increasingly complex. One notable trend involves the transition from a centralized, single-organization environment to a distributed, cross-organizational setting. In such a context, an AI system may need to rely on data and models (including foundational models) from other organizations, which may not be entirely trustworthy. In addition, complex AI models may never be fully explainable, but we continue to use them due to their superior performance. These factors introduce new challenges to technical debt, such as trust debt and explainability debt. This presentation will explore these challenges and suggest approaches for mitigating these technical debts.
Dr. Xiwei (Sherry) Xu is a principal research scientist at CSIRO Data61. She is currently leading the software systems research group (SSRG). Xiwei is a conjoint senior lecturer with UNSW and is responsible for delivering a subject in Computer Science and Engineering School (CSE). Her current research areas include responsible AI (RAI), software engineering for AI-based systems (SE4AI) and designing blockchain applications. Xiwei is identified by the Bibliometric Assessment of Software Engineering Scholars and Institutions (2013-2020) as a top scholar and ranked 4th in the world as the most impactful SE researchers by JSS (Journal of Systems and Software).