Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel and unorder or de-parallelize already parallel streams. The approach was implemented as a plug-in to the Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 11 Java projects consisting of ?642K lines of code. We found that 57 of 157 candidate streams (36.31%) were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential.

Background Software developers are increasingly required to write concurrent code. However, most developers find concurrent programming difficult. To better help developers, it is imperative to understand their interest and difficulties in terms of concurrency topics they encounter often when writing concurrent code.

Streaming APIs are pervasive in mainstream Object-Oriented languages and platforms. For example, the Java 8 Stream API allows for functional-like, MapReduce-style operations in processing both finite, e.g., collections, and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we describe the engineering aspects of an open source automated refactoring tool called Optimize Streams that assists developers in writing optimal stream software in a semantics-preserving fashion. Based on a novel ordering and typestate analysis, the tool is implemented as a plug-in to the popular Eclipse IDE, using both the WALA and SAFE frameworks. The tool was evaluated on 11 Java projects consisting of ~642 thousand lines of code, where we found that 36.31% of candidate streams were refactorable, and an average speedup of 1.55 on a performance suite was observed. We also describe experiences gained from integrating three very different static analysis frameworks to provide developers with an easy-to-use interface for optimizing their stream code to its full potential.

This study investigated user experiences of participants testing a prototype messaging app with automatic speech recognition (ASR). Twelve pairs of participants, where one individual was deaf or hard-of-hearing (DHH), and the other one was hearing used the app, with the hearing individual using speech and ASR and the DHH one using typing. Participants completed a standardized decision making task to test the app. Regardless of hearing status of the participants or the type of device used, participants were generally satisfied with the app. These findings indicate that ASR has potential to facilitate communication between DHH and hearing individuals in small groups and that the technology merits further investigation.

This study investigated use of automatic speech recognition (ASR) in 12 pairs where one individual was deaf or hard-of-hearing (DHH), and the other one was hearing, with the hearing individual using speech and ASR and the DHH one using typing. Each of the pairs used prototype software for messaging to communicate while completing a standardized decision making task. Results suggested that ASR produced text at a faster rate than a keyboard. When both participants used keyboards, they exchanged more messages than when one or both of them used a smartphone with a miniature keyboard.