The rise of social media in the enterprise has enabled new ways for employees to speak up and communicate openly with colleagues. This rich textual data can potentially be mined to better understand the opinions and sentiment of employees for the benefit of the organization. In this paper, we introduce Enterprise Social Pulse (ESP) -- a tool designed to support analysts whose job involves understanding employee chatter. ESP aggregates and analyzes data from internal and external social media sources while respecting employee privacy. It surfaces the data through a user interface that supports organic results and keyword search, data segmentation and filtering, and several analytics and visualization features. An evaluation of ESP was conducted with 19 Human Resources professionals. Results from a survey and interviews with participants revealed the value and willingness to use ESP, but also surfaced challenges around deploying an employee social media listening solution in an organization.

Frameworks and libraries offer reusable and customizable functionality through Application Programming Interfaces (APIs). Correctly using large and sophisticated APIs can represent a challenge due to hidden assumptions and requirements. Numerous approaches have been developed to infer properties of APIs, intended to guide their use by developers. With each approach come new definitions of API properties, new techniques for inferring these properties, and new ways to assess their correctness and usefulness. This paper provides a comprehensive survey of over a decade of research on automated property inference for APIs. Our survey provides a synthesis of this complex technical field along different dimensions of analysis: properties inferred, mining techniques, and empirical results. In particular, we derive a classification and organization of over 60 techniques into five different categories based on the type of API property inferred: unordered usage patterns, sequential usage patterns, behavioral specifications, migration mappings, and general information.

Planning a complex software modification task imposes a high cognitive burden on developers, who must juggle navigating the software, understanding what they see with respect to their task, and deciding how their task should be performed given what they have discovered. Pragmatic reuse tasks, where source code is reused in a white-box fashion, is an example of a complex and error-prone modification task: the developer must plan out which portions of a system to reuse, extract the code, and integrate it into their own system. In this paper we present a recommendation system that automates some aspects of the planning process undertaken by developers during pragmatic reuse tasks. In a retroactive evaluation, we demonstrate that our technique was able to provide the correct recommendation 64% of the time and was incorrect 25% of the time. Our case study suggests that developer investigative behaviour is positively influenced by the use of the recommendation system.