Reid Holmes

Prospective Graduate Students / Postdocs

This faculty member is currently not actively recruiting graduate students or Postdoctoral Fellows, but might consider co-supervision together with another faculty member.

Associate Professor

Research Classification

Research Interests

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

Relevant Degree Programs

Affiliations to Research Centres, Institutes & Clusters


Graduate Student Supervision

Master's Student Supervision (2010 - 2020)
Augmenting source code editors with external information (2019)

Developers use many tools and services to acquire code-centric information in their development processes. These tools, such as continuous integration and coverage platforms, make the development process fluid and help developers make better decisions, but developers often need to leave their code editors to retrieve the in- formation from them. In this thesis, I present External Information in Code Editor (EICE), an external information integration technology probe, to understand how to help developers acquire and present information from external sources. In this thesis, I explore the kinds of external information developers need in their development process and how to present it. As visually changing code editors can affect developers' interaction and productivity, I conduct an online survey to understand developers' preferences on visual representations of external information. Finally, I investigate the benefits and disadvantages of EICE plugins in a user study. The results demonstrate that with careful design, the integration of external information into source code editors can be beneficial to developers.

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Automatic conceptual window grouping with frequent pattern matching (2019)

While working, software developers constantly switch between different projects and tasks and use many different applications, web resources and files. These diverse resources are scattered across many windows and lead to cluttered workspaces that can distract developers in their workflows. Having mechanisms to determine which resources belong together for working on a project, would allow us to develop tools that could support developers in organizing their work, declutter their workspace and switch between projects. Existing approaches in this area often either require users to manually define which resources belong together, or do not examine how users would group the resources themselves and how to best support them.In this thesis we present an approach that automatically detects groups of applications and resources that developers use and are relevant to the tasks and projects they are working on. These groups are referred to as Conceptual Groups. The approach applies frequent pattern analysis on recorded interaction data and clusters these to retrieve conceptual groups. To measure the accuracy of our approach, we conducted a study with 11 participants and compared it to existing approaches which were outperformed by up to 50%.

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CodeShovel: constructing robust source code history (2019)

Source code histories are valuable resources for developers, and development tools, to reason about the evolution of their software systems.Through a survey with 42 professional software developers, we gained insight in how they use the history of their projects and what challenges they face while doing so.We discovered significant mismatches between the output provided by developers' existing approaches and what they need to successfully complete their tasks.To address these shortcomings, we created CodeShovel, a tool for navigating method histories that is able to quickly produce complete method histories in 90% of the cases. CodeShovel enables developers to navigate the entire history of source code methods quickly and reliably, regardless of the transformations and refactorings the method has undergone over its lifetime, helping developers build a robust understanding of its evolution.A field study with 16 industrial developers confirmed our empirical findings of CodeShovel's correctness and efficiency and additionally showed that our approach can be useful for a wide range of industrial development tasks.

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Supporting focused work on window-based desktops (2019)

When working with a computer, information workers continuously switch tasks and applications to complete their work. Given the high fragmentation and complexity of their work, staying focused on the relevant pieces of information can become quite challenging in today's window-based environments, especially with the ever increasing size of display technology. To support workers in staying focused, we conducted a formative study with 18 professional information workers in which we examined their computer based and eye gaze interaction with the window environment and devised a relevance model of open windows. Based on the results, we developed a prototype to dim irrelevant windows and reduce distractions, and evaluated it in a user study. Our results show that participants keep an average of 12 windows open at all times, switch windows every 17 seconds, and that our prototype was able to predict and highlight relevant open windows with high accuracy and was considered helpful by the users.

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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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