NIER Session

Source code identifiers such as classes, methods, and fields appear in many different contexts. For instance, a developer performing a task using the android.app.Activity class could consult various project resources including the class's source file, API documentation, issue tracker, mailing list discussions, code reviews, or questions on Stack Overflow. These information sources are logically connected by the source code elements they describe, but are generally decoupled from each other. This has historically been tolerated by developers, since there was no obvious way to easily navigate between the data sources. However, it is now common for these sources to have web-based front ends that provide a standard mechanism (the browser) for viewing and interacting with the data they contain. Augmenting these front ends with hyperlinks and search would make development easier by allowing developers to quickly navigate between disparate sources of information about the same code element. In this paper, we propose a method of automatically linking disparate information repositories with an emphasis on high precision. We also propose a method of augmenting web-based front ends with these links to make it easier for developers to quickly gain a comprehensive view of the source code elements they are investigating. Research challenges include identifying source code tokens in the midst of natural language text and incomplete code fragments, dynamically augmenting the web views of the data repositories, and supporting novel composition of the link data to provide comprehensive views for specific source code elements.

Recent work on workspace monitoring allows conflict pre- diction early in the development process, however, these approaches mostly use syntactic differencing techniques to compare different program versions. In contrast, traditional change-impact analysis techniques analyze related versions of the program only after the code has been checked into the master repository. We propose a novel approach, DeCAF (Development Context Analysis Framework), that leverages the development context to scope a change impact analysis technique. The goal is to characterize the impact of each developer on other developers in the team. There are various client applications such as task prioritization, early conflict detection, and providing advice on testing that can benefit from such a characterization. The DeCAF frame- work leverages information from the development context to bound the iDiSE change impact analysis technique to analyze only the parts of the code base that are of interest. Bounding the analysis can enable DeCAF to efficiently com- pute the impact of changes using a combination of program dependence and symbolic execution based approaches.

Traditional integrated development environments (IDEs) provide developers with robust environments for writing, testing, debugging, and deploying code. As the world becomes increasingly networked and more services are delivered via the cloud, it is only natural that the functionality of IDEs be delivered via the cloud. In addition to simplifying the provisioning and deployment of new IDE features, and making it easier to integrate with other web native tools, cloud based IDEs provide some fundamental advantages when it comes to understanding the behavior of a wide community of software developers. One of these advantages for the IDE provider is the ability to transparently monitor and analyze the real-time fine-grained actions of a large number of developers. In this paper, we explore how to leverage these transparent monitoring capabilities of cloud based IDEs to develop advanced analytics to understand developers' behavior and infer their characteristics. We demonstrate the feasibility of this research direction with a preliminary study focusing on the way that source code files grow for different developers, development tasks, and skill levels. We then analyze the trends of source code file growth and find growth is more similar within subjects than within tasks.

First responders and others operating in crisis environments increasingly make use of handheld devices to help with tasks such as face recognition, language translation, decision-making and mission planning. These resource-constrained edge environments are characterized by dynamic context, limited computing resources, high levels of stress, and intermittent network connectivity. Cyber-foraging is the leverage of external resource-rich surrogates to augment the capabilities of resource-limited devices. In cloudlet-based cyber-foraging, resource-intensive computation is offloaded to cloudlets – discoverable, generic servers located in single-hop proximity of mobile devices. This paper presents several strategies for cloudlet-based cyber-foraging and encourages research in this area to consider a tradeoff space beyond energy, performance and fidelity of results.

This paper introduces the Cloud Efficiency (CE) metric, a novel runtime metric which assesses how effectively an application uses software-defined infrastructure. The CE metric is computed as the ratio of two functions: i) a benefit function which captures the current set of benefits derived from the application, and ii) a cost function which describes the current charges incurred by the application's resources. We motivate the need for the CE metric, describe in further detail how to compute it, and present experimental results demonstrating its calculation.