Many Duplicate Bug Report Detection (DBRD) techniques have been proposed in the research literature. The industry uses some other techniques. Unfortunately, there is insufficient comparison among them, and it is unclear how far we have been. This work fills this gap by comparing the aforementioned techniques. To compare them, we first need a benchmark that can estimate how a tool would perform if applied in a realistic setting today. Thus, we first investigated potential biases that affect the fair comparison of the accuracy of DBRD techniques. Our experiments suggest that data age and issue tracking system (ITS) choice cause a significant difference. Based on these findings, we prepared a new benchmark. We then used it to evaluate DBRD techniques to estimate better how far we have been. Surprisingly, a simpler technique outperforms recently proposed sophisticated techniques on most projects in our benchmark. In addition, we compared the DBRD techniques proposed in research with those used in Mozilla and VSCode . Surprisingly, we observe that a simple technique already adopted in practice can achieve comparable results as a recently proposed research tool. Our study gives reflections on the current state of DBRD, and we share our insights to benefit future DBRD research.

Developers often ask for libraries that implement specific mathematical expressions. A fundamental bottleneck in building information retrieval (IR) systems to answer such mathematical queries is the inability to detect a given expression in software binaries. While we have a few math IR solutions such as EgoMath2 and Tangent-3 that work over text documents, none exist to search over software binaries. Our vision is to build a search system for binaries to answer queries containing mathematical expressions. A wide variety of compilers and differences in the way they optimize the code, pose difficult challenges to solve this problem. In this work, we discuss our preliminary results in detecting mathematical expressions in software binaries. We use a knowledge base assisted approach to solve this problem. We are able to search mathematical expressions with a precision of 80% and a recall of 53%. This work opens up interesting research opportunities in areas such as software security and performance, to help analysts in identifying and analyzing binaries for implementations of mathematical expressions.

Advances in text retrieval do not apply directly to source code retrieval because of the difference in characteristics of source code when compared to text. Recently, researchers have proposed specialized indexing and querying techniques that are based on structural representations and semantics of source code. Current tools and techniques heavily depend on user defined terms in source code to leverage text retrieval techniques. Further, they have limitations in handling partial programs, platform independence and index-time processing. We focus on building reusable models of source code for the purposes of indexing and querying.

Code search with natural language terms performs poorly because programming concepts do not always lexically match their syntactic forms. For example, in Java, the programming concept "array" does not match with its syntactic representation of "[ ]". Code search engines can assist developers more effectively over natural language queries if such mappings existed for a variety of programming languages. In this work, we present a programming language agnostic technique to discover such mappings between syntactic forms and natural language terms representing programming concepts. We use the questions and answers in Stack Overflow to create this mapping. We implement our approach in a tool called ANNE. To evaluate its effectiveness, we conduct a user study in an academic setting in which teaching assistants use ANNE to search for code snippets in student submissions. With the use of ANNE, we find that the participants are 29% quicker with no significant drop in correctness and completeness.

Software developers rarely write code from scratch. With the existence of Wikipedia, discussion forums, books and blogs, it is hard to imagine a software developer not looking up these sites for sample code while building any non-trivial software system. While researchers have proposed approaches to retrieve relevant posts and code snippets, the need for finding variant implementations of functionally similar code snippets has been ignored. In this work, we propose an approach to automatically create a repository of structurally heterogeneous but functionally similar source code examples from unstructured sources. We evaluate the approach on stackoverflow, a discussion forum that has approximately 19 million posts. The results of our evaluation indicates that the approach extracts structurally different snippets with a precision of 83%. A repository of such heterogeneous source code examples will be useful to programmers in learning different implementation strategies and for researchers working on problems such as program comprehension, semantic clones and code search.

Developers deal with the persistent problem of understanding non-trivial code snippets. To understand the given implementation, its issues, and available choices, developers will benefit from reading relevant discussions and descriptions over the web. However, there is no easy way to know the relevant natural language terms so as to reach to such descriptions from a code snippet, especially if the documentation is inadequate and if the vocabulary used in the code is not helpful for web search. We propose an approach to solve this problem using a repository of topics and associated structurally variant snippets collected from a discussion forum. In this on-going work, we take Java methods from the code samples of three Java books, match them with the repository, and associate the topics with 76.9% precision and 66.7% recall.