Software development environments (IDEs) have not followed the IT industry's inexorable trend towards distribution. They do too little to address the problems raised by today's increasingly distributed projects; neither do they facilitate collaborative and interactive development practices. A consequence is the continued reliance of today's IDEs on paradigms such as traditional configuration management, which were developed for earlier modes of operation and hamper collaborative projects. This contribution describes a new paradigm: cloud-based development, which caters to the specific needs of distributed and collaborative projects. The CloudStudio IDE embodies this paradigm by enabling developers to work on a shared project repository. Configuration management becomes unobtrusive; it replaces the explicit update-modify-commit cycle by interactive editing and real-time conflict tracking and management. A case study involving three teams of pairs demonstrates the usability of CloudStudio and its advantages for collaborative software development over traditional configuration management practices.
A different small group of research tools such as Syde @cite_4 and CollabVS @cite_3 introduce new models of CM, where more abstract and flexible change analyses are possible. Syde works on abstract syntax trees of the code, and defines changes as abstract operations on trees. Crystal @cite_14 is based on the idea of constantly trying to merge the software artifacts of different developers in order to detect conflicts as early as possible. These concepts are quite novel, yet still ultimately centered on the notion of conflict (and conflict resolution). CloudStudio's focus is instead on providing programmers with real-time code change awareness, which can prevent many conflicts from arising in the first place.
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Abstract Synchronous programming (Berry, 1989) is a powerful approach to programming reactive systems. Following the idea that “processes are relations extended over time” (Abramsky, 1993), we propose a simple but powerful model for timed, determinate computation, extending the closure-operator model for untimed concurrent constraint programming (CCP). In . (1994a) we had proposed a model for this called tcc—here we extend the model of tcc to express strong time-outs: if an event A does not happen through time t , cause event B to happen at time t . Such constructs arise naturally in practice (e.g. in modeling transistors) and are supported in synchronous programming languages. The fundamental conceptual difficulty posed by these operations is that they are non-monotonic. We provide compositional semantics to the non-monotonic version of concurrent constraint programming (Default cc) obtained by changing the underlying logic from intuitionistic logic to Reiter's default logic. This allows us to use the same construction (uniform extension through time) to develop Default cc as we had used to develop tcc from cc. Indeed the smooth embedding of cc processes into Default cc processes lifts to a smooth embedding of tcc processes into Default cc processes. We identify a basic set of combinators (that constitute the Default cc programming framework), and provide constructive operational semantics (implemented by us as an interpreter) for which the model is fully abstract. We show that the model is expressive by defining combinators from the synchronous languages. We show that Default cc is compositional and supports the properties of multiform time, orthogonal pre-emption and executable specifications. In addition, Default cc programs can be read as logical formulae (in an intuitionistic temporal logic)—we show that this logic is sound and complete for reasoning about (in)equivalence of Default cc programs. Like the synchronous languages, Default cc programs can be compiled into finite state automata. In addition, the translation can be specified compositionally. This enables separate compilation of Default cc programs and run-time tradeoffs between partial compilation and interpretation. A preliminary version of this paper was published as . (1995). Here we present a complete treatment of hiding, along with a detailed treatment of the model. Abstract A simple, fully automated, and efficient method to determine the structural properties and evolution (tracking) of cloud shields of convective systems (CS) is described. The method, which is based on the maximum spatial correlation tracking technique (MASCOTTE), is a new alternative to the existent techniques available for studies that monitor the evolution of CS using satellite images. MASCOTTE provides as CS structural properties the following parameters: mean and variance of brightness temperature, horizontal area, perimeter, minimum brightness temperature, fractional convective area, center of gravity, and fragmentation. The fragmentation parameter has the potential to monitor the evolution of the CS. A new way of estimating the orientation and eccentricity of CS is proposed and is based on the empirical orthogonal function analysis of CS pixel coordinates. The method includes an accurate detection of splitting and merging of convective systems, which is a critical step in the automated satel... In this paper we present a novel street scene semantic recognition framework, which takes advantage of 3D point clouds captured by a high-definition LiDAR laser scanner. An important problem in object recognition is the need for sufficient labeled training data to learn robust classifiers. In this paper we show how to significantly reduce the need for manually labeled training data by reduction of scene complexity using non-supervised ground and building segmentation. Our system first automatically segments grounds point cloud, this is because the ground connects almost all other objects and we will use a connect component based algorithm to oversegment the point clouds. Then, using binary range image processing building facades will be detected. Remained point cloud will grouped into voxels which are then transformed to super voxels. Local 3D features extracted from super voxels are classified by trained boosted decision trees and labeled with semantic classes e.g. tree, pedestrian, car, etc. The proposed method is evaluated both quantitatively and qualitatively on a challenging fixed-position Terrestrial Laser Scanning (TLS) Velodyne data set and two Mobile Laser Scanning (MLS), Paris-rue-Madam and NAVTEQ True databases. Robust scene parsing results are reported. Software defect prediction, which predicts defective code regions, can help developers find bugs and prioritize their testing efforts. To build accurate prediction models, previous studies focus on manually designing features that encode the characteristics of programs and exploring different machine learning algorithms. Existing traditional features often fail to capture the semantic differences of programs, and such a capability is needed for building accurate prediction models. To bridge the gap between programs' semantics and defect prediction features, this paper proposes to leverage a powerful representation-learning algorithm, deep learning, to learn semantic representation of programs automatically from source code. Specifically, we leverage Deep Belief Network (DBN) to automatically learn semantic features from token vectors extracted from programs' Abstract Syntax Trees (ASTs). Our evaluation on ten open source projects shows that our automatically learned semantic features significantly improve both within-project defect prediction (WPDP) and cross-project defect prediction (CPDP) compared to traditional features. Our semantic features improve WPDP on average by 14.7 in precision, 11.5 in recall, and 14.2 in F1. For CPDP, our semantic features based approach outperforms the state-of-the-art technique TCA+ with traditional features by 8.9 in F1.