Working code examples are useful resources for pragmatic reuse in software development. A working code example provides a solution to a specific programming problem. Earlier studies have shown that existing code search engines are not successful in finding working code examples. They fail in ranking high quality code examples at the top of the result set. To address this shortcoming, a variety of pattern-based solutions are proposed in the literature. However, these solutions cannot be integrated seamlessly in Internet-scale source code engines due to their high time complexity or query language restrictions. In this paper, we propose an approach for spotting working code examples which can be adopted by Internet-scale source code search engines. The time complexity of our approach is as low as the complexity of existing code search engines on the Internet and considerably lower than the pattern-based approaches supporting free-form queries. We study the performance of our approach using a representative corpus of 25,000 open source Java projects. Our findings support the feasibility of our approach for Internet-scale code search. We also found that our approach outperforms Ohloh Code search engine, previously known as Koders, in spotting working code examples.

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Finding lines of code similar to a code fragment across large knowledge bases in fractions of a second is a new branch of code clone research also known as real-time code clone search. Among the requirements real-time code clone search has to meet are scalability, short response time, scalable incremental corpus updates, and support for type-1, type-2, and type-3 clones. We conducted a set of empirical studies on a large open source code corpus to gain insight about its characteristics. We used these results to design and optimize a multi-level indexing approach using hash table-based and binary search to improve Internet-scale real-time code clone search response time. Finally, we performed an evaluation on an Internet-scale corpus (1.5 million Java files and 266 MLOC). Our approach maintains a response time for 99.9% of clone searches in the microseconds range, while supporting the aforementioned requirements.