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Groupware systems are computer-based systems that support two or more users engaged in a common task, and that provide an interface to a shared environment. These systems frequently require fine-granularity sharing of data and fast response times. This paper distinguishes real-time groupware systems from other multiuser systems and discusses their concurrency control requirements. An algorithm for concurrency control in real-time groupware systems is then presented. The advantages of this algorithm are its simplicity of use and its responsiveness: users can operate directly on the data without obtaining locks. The algorithm must know some semantics of the operations. However the algorithm's overall structure is independent of the semantic information, allowing the algorithm to be adapted to many situations. An example application of the algorithm to group text editing is given, along with a sketch of its proof of correctness in this particular case. We note that the behavior desired in many of these systems is non-serializable.

Abstract. Groupware systems are computer-based systems that support two or more users engaged in a common task, and that provide an interface to a shared environment. These systems frequently require fine-granularity sharing of data and fast response times. This paper distinguishes real-time groupware systems from other multi-user systems and discusses their concurrency control requirements. An algorithm for concurrency control in real-time groupware systems is then presented. The advantages of this algorithm are its simplicity of use and its responsiveness: users can operate directly on the data without obtaining locks. The algorithm must know some semantics of the operations. However the algorithm's overall structure is independent of the semantic information, allowing the algorithm to be adapted to many situations. An example application of the algorithm to group text editing is given, along with a sketch of its proof of correctness in this particular case. We note that the behavior desired in many of these systems is non-serializable.

JavaScript is widely used for writing client-side web applications and is getting increasingly popular for writing mobile applications. However, unlike C, C++, and Java, there are not that many tools available for analysis and testing of JavaScript applications. In this paper, we present a simple yet powerful framework, called Jalangi, for writing heavyweight dynamic analyses. Our framework incorporates two key techniques: 1) selective record-replay, a technique which enables to record and to faithfully replay a user-selected part of the program, and 2) shadow values and shadow execution, which enables easy implementation of heavy-weight dynamic analyses. Our implementation makes no special assumption about JavaScript, which makes it applicable to realworld JavaScript programs running on multiple platforms. We have implemented concolic testing, an analysis to track origins of nulls and undefined, a simple form of taint analysis, an analysis to detect likely type inconsistencies, and an object allocation profiler in Jalangi. Our evaluation of Jalangi on the SunSpider benchmark suite and on five web applications shows that Jalangi has an average slowdown of 26X during recording and 30X slowdown during replay and analysis. The slowdowns are comparable with slowdowns reported for similar tools, such as PIN and Valgrind for x86 binaries. We believe that the techniques proposed in this paper are applicable to other dynamic languages.

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