We are on the cusp of a major opportunity: software tools that take advantage of Big Code. Specifically, Big Code will enable novel tools in areas such as security enhancers, bug finders, and code synthesizers. What do researchers need from Big Code to make progress on their tools? Our answer is an infrastructure that consists of 100,000 executable Java programs together with a set of working tools and an environment for building new tools.
Spacetime is a framework for developing time-stepped, multi-worker applications based on the tuplespace model. Workers compute within spacetimed frames -- a fixed portion of the shared data during a fixed period of time. The locally modified data may be pushed back to the shared store at the end of each step.
Given the availability of large-scale source-code repositories, there have been a large number of applications for clone detection. Unfortunately, despite a decade of active research, there is a marked lack in clone detectors that scale to large software repositories. In particular for detecting near-miss clones where significant editing activities may take place in the cloned code.
We developed a token-based approach for large scale code clone detection which is based on a filtering heuristic that reduces the number of token comparisons when the two code blocks are compared. We also developed a MapReduce based parallel algorithm that uses the filtering heuristic and scales to thousands of projects. The filtering heuristic is generic and can also be used in conjunction with other token-based approaches. In that context, we demonstrated how it can increase the retrieval speed and decrease the memory usage of the index-based approaches.
Yelp reviews and ratings are important source of information to make informed decisions about a venue. We conjecture that further classification of yelp reviews into relevant categories can help users to make an informed decision based on their personal preferences for categories. Moreover, this aspect is especially useful when users do not have time to read many reviews to infer the popularity of venues across these categories.
Sourcerer is an ongoing research project at the University of California, Irvine aimed at exploring open source projects through the use of code analysis. The existence of an extremely large body of open source code presents a tremendous opportunity for software engineering research. Not only do we leverage this code for our own research, but we provide the open source Sourcerer Infrastructure and curated datasets for other researchers to use.
The Sourcerer Infrastructure is composed of a number of layers.
ISR
