Version Control in Online Software Repositories

Software version control repositories provide a uniform and stable interface to manage documents and their version histories. Unfortunately, Open Source systems, for example, CVS, Subversion, and GNU Arch are not well suited to highly collaborative environments and fail to track semantic changes in repositories. We introduce document provenance as our Description Logic framework to track the semantic changes in software repositories and draw interesting results about their historic behaviour using a rule-based inference engine. To support the use of this framework, we have developed our own online collaborative tool, leveraging the fluency of the modern WikiWikiWeb

Version Control in Online Software Repositories

Software version control repositories provide a uniform and stable interface to manage documents and their version histories. Unfortunately, Open Source systems, for example, CVS, Subversion, and GNU Arch are not well suited to highly collaborative environments and fail to track semantic changes in repositories. We introduce document provenance as our Description Logic framework to track the semantic changes in software repositories and draw interesting results about their historic behaviour using a rule-based inference engine. To support the use of this framework, we have developed our own online collaborative tool, leveraging the fluency of the modern WikiWikiWeb

Version control of pathway models using XML patches

<p>Background: Computational modelling has become an important tool in understanding biological systems such as signalling pathways. With an increase in size complexity of models comes a need for techniques to manage model versions and their relationship to one another. Model version control for pathway models shares some of the features of software version control but has a number of differences that warrant a specific solution.</p> <p>Results: We present a model version control method, along with a prototype implementation, based on XML patches. We show its application to the EGF/RAS/RAF pathway.</p> <p>Conclusion: Our method allows quick and convenient storage of a wide range of model variations and enables a thorough explanation of these variations. Trying to produce these results without such methods results in slow and cumbersome development that is prone to frustration and human error.</p&gt

Finding Regressions in Projects under Version Control Systems

Version Control Systems (VCS) are frequently used to support development of large-scale software projects. A typical VCS repository of a large project can contain various intertwined branches consisting of a large number of commits. If some kind of unwanted behaviour (e.g. a bug in the code) is found in the project, it is desirable to find the commit that introduced it. Such commit is called a regression point. There are two main issues regarding the regression points. First, detecting whether the project after a certain commit is correct can be very expensive as it may include large-scale testing and/or some other forms of verification. It is thus desirable to minimise the number of such queries. Second, there can be several regression points preceding the actual commit; perhaps a bug was introduced in a certain commit, inadvertently fixed several commits later, and then reintroduced in a yet later commit. In order to fix the actual commit it is usually desirable to find the latest regression point. The currently used distributed VCS contain methods for regression identification, see e.g. the git bisect tool. In this paper, we present a new regression identification algorithm that outperforms the current tools by decreasing the number of validity queries. At the same time, our algorithm tends to find the latest regression points which is a feature that is missing in the state-of-the-art algorithms. The paper provides an experimental evaluation of the proposed algorithm and compares it to the state-of-the-art tool git bisect on a real data set

Real-time remote control car racing system (PC Version)

The explosive growth of computer technology of today has broadened the minds of computer programmers and electronic engineers. Today, people from both industries are joining forces to create state of the art technology games at the international market. Therefore, we as future technopreneurs need to take this opportunity to learn about both industries and integrate them to make a useful product. In addition, this study is a new game concept which has combined with remote control hobby to create a new car racing game in the future. The idea is to use a real situation in gaming where by combining a few hardware devices (The Revolutionizer) and a software system (Speed Demon ver1.0 Beta) to make it run. The software design is being conducted using Unified Modeling Language (UML) 2.0 and being coded into Visual C++ programming language. The implementation stage is by using two types of R/C car (electric and gas) and the project has been successfully developed into a prototype product. The project is focused more on the R/C electric version. Overall, the project is now completed and ready to be market around the world. This is because the prototype which has been developed in this project has a high market value. Because of this, a business plan which contains all the information that a business plan needs to have for example, description of the product, industry analysis, marketing strategies, and the competitors. As a conclusion, this thesis is considered a unique of its kind in the information technology industry and the pioneer of turning research into money makin

DataHub: Collaborative Data Science & Dataset Version Management at Scale

Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving users the ability to create, branch, merge, difference and search large, divergent collections of datasets, and (b) a platform, DataHub, that gives users the ability to perform collaborative data analysis building on this version control system. We outline the challenges in providing dataset version control at scale.Comment: 7 page