Gilligan: Pragmatic software reuse
Software reuse typically involves reusing components specifically designed for reuse; these components must provide the exact functionality that a developer requires and must fit within their system without structural or behavioural mismatch. When these two conditions are not met, developers have the opportunity to investigate pragmatic software reuse approaches; in these situations the software being reused may not have been designed specifically for reuse, but may enable the developer to greatly reduce the time and cost of developing similar software from scratch. In these cases developers must extract some portion of the source code, modify it to suit their needs, and integrate it into their own system.
To address shortcomings in performing these tasks manually, I created the Gilligan pragmatic-reuse environment [CV5, CV10, CV2, CV17]. Developers using my planning tool were able to locate twice as many relevant program elements compared to traditional approaches [CV10]; they needed to resolve 98% fewer compilation errors [CV2]; they were 32% more likely to successfully complete a pragmatic reuse task [CV17]; and they took significantly less time to perform their pragmatic reuse tasks [CV17].
DeepIntellisense: Surfacing context-sensitive information in IDEs
Software engineers working in large teams on large, long-lived code-bases have trouble understanding why the source code looks the way does. Often, they answer their questions by looking at past revisions of the source code, bug reports, code checkins, mailing list messages, and other documentation. This process of inquiry can be quite inefficient, especially when the answers they seek are located in isolated repositories accessed by multiple independent investigation tools. Prior mining approaches have focused on linking various data repositories together; in this paper we investigate techniques for displaying information extracted from the repositories in a way that helps developers to build a cohesive mental model of the rationale behind the code. After interviewing several developers and testers about how they investigate source code, we created a Visual Studio plugin called Deep Intellisense that summarizes and displays historical information about source code. We designed Deep Intellisense to address many of the hurdles engineers face with their current techniques, and help them spend less time gathering information and more time getting their work done.
PopCon: Identifying important API
Eclipse has evolved from a fledgling Java IDE into a mature software ecosystem. One of the greatest benefits Eclipse provides developers is flexibility; however, this is not without cost. New Eclipse developers often find the framework to be large and confusing. Determining which parts of the framework they should be using can be a difficult task as Eclipse documentation tends to be either very high-level, focusing on the design of the framework, or low-level, focusing on specific APIs. We have developed a tool called PopCon that provides a bridge between high-level design documentation and low-level API documentation by statically analyzing a framework and several of its clients and providing a ranked list of the relative popularity of its APIs. We have applied PopCon to the Eclipse framework for this challenge to help newbie Eclipse developers identify some of the most relevant APIs for their tasks.
Strathcona: Context-sensitive example recommendation
When coding to an application programming interface (API), developers often encounter difficulties, unsure of which class to subclass, which objects to instantiate, and which methods to call. Example source code that demonstrates the use of the API can help developers make progress on their task. This paper describes an approach to provide such examples in which the structure of the source code that the developer is writing is matched heuristically to a repository of source code that uses the API. The structural context needed to query the repository is extracted automatically from the code, freeing the developer from learning a query language or from writing their code in a particular style. The repository is generated automatically from existing applications, avoiding the need for handcrafted examples. We demonstrate that the approach is effective, efficient, and more reliable than traditional alternatives through four empirical studies.