Reid Holmes

Associate Professor

Research Interests

computer science
software engineering
software testing
software quality
open source software
software development tools
software comprehension
static analysis

Relevant Degree Programs

 

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Master's students
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Graduate Student Supervision

Master's Student Supervision (2010 - 2018)
Context-aware conversational developer assistants (2018)

Building and maintaining modern software systems requires developers to perform a variety of tasks that span various tools and information sources. The crosscutting nature of these development tasks requires developers to maintain complex mental models and forces them (a) to manually split their high-level tasks into low-level commands that are supported by the various tools, and (b) to (re)establish their current context in each tool. In this thesis I present Devy, a Conversational Developer Assistant (CDA) that enables developers to focus on their high-level development tasks. Devy reduces the number of manual, often complex, low-level commands that developers need to perform, freeing them to focus on their high-level tasks. Specifically, Devy infers high-level intent from developer's voice commands and combines this with an automatically-generated context model to determine appropriate workflows for invoking low-level tool actions; where needed, Devy can also prompt the developer for additional information. Through a mixed methods evaluation with 21 industrial developers, we found that Devy provided an intuitive interface that was able to support many development tasks while helping developers stay focused within their development environment. While industrial developers were largely supportive of the automation Devy enabled, they also provided insights into several other tasks and workflows CDAs could support to enable them to better focus on the important parts of their development tasks.

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Enabling configuration self-adaptation using machine learning (2018)

Due to advancements in distributed systems and the increasing industrial demands placed on these systems, distributed systems are comprised of multiple complex components (e.g databases and their replication infrastructure, caching components, proxies, and load balancers) each of which have their own complex configuration parameters that enable them to be tuned for given runtime requirements. Software Engineers must manually tinker with many of these configuration parameters that change the behaviour and/or structure of the system in order to achieve their system requirements. In many cases, static configuration settings might not meet certain demands in a given context and ad hoc modifications of these configuration parameters can trigger unexpected behaviours, which can have negative effects on the quality of the overall system.In this work, I show the design and analysis of Finch; a tool that injects a machine learning based MAPE-K feedback loop to existing systems to automate how these configuration parameters are set. Finch configures and optimizes the system to meet service-level agreements in uncertain workloads and usage patterns. Rather than changing the core infrastructure of a system to fit the feedback loop, Finch asks the user to perform a small set of actions: instrumenting the code and configuration parameters, defining service-level objectives and agreements, and enabling programmatic changes to these configurations. As a result, Finch learns how to dynamically configure the system at runtime to self-adapt to its dynamic workloads.I show how Finch can replace the trial-and-error engineering effort that otherwise would be spent manually optimizing a system's wide array of configuration parameters with an automated self-adaptive system.

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