No abstract

Today's shared-memory parallel programming models are complex and error-prone.While many parallel programs are intended to be deterministic, unanticipated thread interleavings can lead to subtle bugs and nondeterministic semantics. In this paper, we demonstrate that a practical type and effect system can simplify parallel programming by guaranteeing deterministic semantics with modular, compile-time type checking even in a rich, concurrent object-oriented language such as Java. We describe an object-oriented type and effect system that provides several new capabilities over previous systems for expressing deterministic parallel algorithms.We also describe a language called Deterministic Parallel Java (DPJ) that incorporates the new type system features, and we show that a core subset of DPJ is sound. We describe an experimental validation showing thatDPJ can express a wide range of realistic parallel programs; that the new type system features are useful for such programs; and that the parallel programs exhibit good performance gains (coming close to or beating equivalent, nondeterministic multithreaded programs where those are available).

Continuous integration (CI) systems automate the compilation, building, and testing of software. Despite CI rising as a big success story in automated software engineering, it has received almost no attention from the research community. For example, how widely is CI used in practice, and what are some costs and benefits associated with CI? Without answering such questions, developers, tool builders, and researchers make decisions based on folklore instead of data.In this paper, we use three complementary methods to study the usage of CI in open-source projects. To understand which CI systems developers use, we analyzed 34,544 opensource projects from GitHub. To understand how developers use CI, we analyzed 1,529,291 builds from the most commonly used CI system. To understand why projects use or do not use CI, we surveyed 442 developers. With this data, we answered several key questions related to the usage, costs, and benefits of CI. Among our results, we show evidence that supports the claim that CI helps projects release more often, that CI is widely adopted by the most popular projects, as well as finding that the overall percentage of projects using CI continues to grow, making it important and timely to focus more research on CI. CCS Concepts•Software and its engineering → Agile software development; Software testing and debugging;Keywords continuous integration; mining software repositories ASE'16, September 3-7, 2016, Singapore, Singapore c 2016 ACM. 978-1-4503-3845-5/16/09...$15.00 http://dx.

Frameworks and libraries change their APIs. Migrating an application to the new API is tedious and disrupts the development process. Although some tools and ideas have been proposed to solve the evolution of APIs, most updates are done manually. To better understand the requirements for migration tools, we studied the API changes of four frameworks and one library. We discovered that the changes that break existing applications are not random, but tend to fall into particular categories. Over 80% of these changes are refactorings. This suggests that refactoring-based migration tools should be used to update applications.way to update component-based applications. This paper is the beginning of our quest to meet the needs of both component and application developers. What is a suitable representation for the changes that happened in a component? Can it be gathered automatically? Does this representation carry both the syntax and the semantics of changes? Can it lead to safe, automatic updating of component-based applications? How much of the effort spent on updating component-based applications can be saved?Although there are principles of software evolution that are true for software in any language, programming languages have an impact on software evolution. We are particularly interested in the evolution of object-oriented components (we refer to both the library and framework as components, unless a distinction is necessary). Classes contain a mixture of private and public methods. The public methods are those that are meant to be used by application programmers. The set of public methods of a class library make up its application programmer interface (API). Changes to private methods and classes do not pose a problem to application developers; they only care about changes to the API.An important kind of change to object-oriented software is a refactoring [4]. Refactorings are program transformations that change the structure of a program but not its behavior. Refactorings include renaming classes or methods, moving methods or variables between classes, and splitting methods or classes. A refactoring that changes the interface of an object must change all of its clients to use the new interface. When a class library that is reused in many systems is refactored, the systems that reuse it must change. However, those developing the library often do not know all of the systems that reuse it. The new version of the library is a refactoring from their point of view, but not from the point of view of the application developers who are their customers.The original work on refactoring was motivated by framework evolution. Opdyke and Johnson [5] looked at the Choices operating system and the kind of refactorings that occurred as it evolved. Graver [6] studied an object-oriented compiler framework as it went through three iterations. Tokuda and Batory [7] describe the evolution of two frameworks, focusing on how large architectural changes can be accomplished by a sequence of refactorings.However, none of these studies d...

Abstract.One of the costs of reusing software components is updating applications to use the new version of the components. Updating an application can be error-prone, tedious, and disruptive of the development process. Our previous study showed that more than 80% of the disruptive changes in five different components were caused by refactorings. If the refactorings that happened between two versions of a component could be automatically detected, a refactoring tool could replay them on applications. We present an algorithm that detects refactorings performed during component evolution. Our algorithm uses a combination of a fast syntactic analysis to detect refactoring candidates and a more expensive semantic analysis to refine the results. The experiments on components ranging from 17 KLOC to 352 KLOC show that our algorithm detects refactorings in real-world components with accuracy over 85%.