Big Code Search: A Bibliography

peer reviewedCode search is an essential task in software development. Developers often search the internet and other code databases for necessary source code snippets to ease the development efforts. Code search techniques also help learn programming as novice programmers or students can quickly retrieve (hopefully good) examples already used in actual software projects. Given the recurrence of the code search activity in software development, there is an increasing interest in the research community. To improve the code search experience, the research community suggests many code search tools and techniques. These tools and techniques leverage several different ideas and claim a better code search performance. However, it is still challenging to illustrate a comprehensive view of the field considering that existing studies generally explore narrow and limited subsets of used components. This study aims to devise a grounded approach to understanding the procedure for code search and build an operational taxonomy capturing the critical facets of code search techniques. Additionally, we investigate evaluation methods, benchmarks, and datasets used in the field of code search

Exploiting Context in Dealing with Programming Errors and Exceptions in the IDE

Studies show that software developers spend about 19% of their development time in web surfing. While collecting necessary information using traditional web search, they face several practical challenges. First, it does not consider context (i.e., surroundings, circumstances) of the programming problems during search unless the developers do so in search query formulation, and forces the developers to frequently switch between their working environment (e.g., IDE) and the web browser. Second, technical details (e.g., stack trace) of an encountered exception often contain a lot of information, and they cannot be directly used as a search query given that the traditional search engines do not support long queries. Third, traditional search generally returns hundreds of search results, and the developers need to manually analyze the result pages one by one in order to extract a working solution. Both manual analysis of a page for content relevant to the encountered exception (and its context) and working an appropriate solution out are non-trivial tasks. Traditional code search engines share the same set of limitations of the web search ones, and they also do not help much in collecting the code examples that can be used for handling the encountered exceptions. In this thesis, we present a context-aware and IDE-based approach that helps one overcome those four challenges above. In our first study, we propose and evaluate a context-aware meta search engine for programming errors and exceptions. The meta search collects results for any encountered exception in the IDE from three popular search engines- Google, Bing and Yahoo and one programming Q & A site- StackOverflow, refines and ranks the results against the detailed context of the encountered exception, and then recommends them within the IDE. From this study, we not only explore the potential of the context-aware and meta search based approach but also realize the significance of appropriate search queries in searching for programming solutions. In the second study, we propose and evaluate an automated query recommendation approach that exploits the technical details of an encountered exception, and recommends a ranked list of search queries. We found the recommended queries quite promising and comparable to the queries suggested by experts. We also note that the support for the developers can be further complemented by post-search content analysis. In the third study, we propose and evaluate an IDE-based context-aware content recommendation approach that identifies and recommends sections of a web page that are relevant to the encountered exception in the IDE. The idea is to reduce the cognitive effort of the developers in searching for content of interest (i.e., relevance) in the page, and we found the approach quite effective through extensive experiments and a limited user study. In our fourth study, we propose and evaluate a context-aware code search engine that collects code examples from a number of code repositories of GitHub, and the examples contain high quality handlers for the exception of interest. We validate the performance of each of our proposed approaches against existing relevant literature and also through several mini user studies. Finally, in order to further validate the applicability of our approaches, we integrate them into an Eclipse plug in prototype--ExcClipse. We then conduct a task-oriented user study with six participants, and report the findings which are significantly promising

Supporting Source Code Search with Context-Aware and Semantics-Driven Query Reformulation

Software bugs and failures cost trillions of dollars every year, and could even lead to deadly accidents (e.g., Therac-25 accident). During maintenance, software developers fix numerous bugs and implement hundreds of new features by making necessary changes to the existing software code. Once an issue report (e.g., bug report, change request) is assigned to a developer, she chooses a few important keywords from the report as a search query, and then attempts to find out the exact locations in the software code that need to be either repaired or enhanced. As a part of this maintenance, developers also often select ad hoc queries on the fly, and attempt to locate the reusable code from the Internet that could assist them either in bug fixing or in feature implementation. Unfortunately, even the experienced developers often fail to construct the right search queries. Even if the developers come up with a few ad hoc queries, most of them require frequent modifications which cost significant development time and efforts. Thus, construction of an appropriate query for localizing the software bugs, programming concepts or even the reusable code is a major challenge. In this thesis, we overcome this query construction challenge with six studies, and develop a novel, effective code search solution (BugDoctor) that assists the developers in localizing the software code of interest (e.g., bugs, concepts and reusable code) during software maintenance. In particular, we reformulate a given search query (1) by designing novel keyword selection algorithms (e.g., CodeRank) that outperform the traditional alternatives (e.g., TF-IDF), (2) by leveraging the bug report quality paradigm and source document structures which were previously overlooked and (3) by exploiting the crowd knowledge and word semantics derived from Stack Overflow Q&A site, which were previously untapped. Our experiment using 5000+ search queries (bug reports, change requests, and ad hoc queries) suggests that our proposed approach can improve the given queries significantly through automated query reformulations. Comparison with 10+ existing studies on bug localization, concept location and Internet-scale code search suggests that our approach can outperform the state-of-the-art approaches with a significant margin

Automatic query reformulation for code search using crowdsourced knowledge

Ministry of Education, Singapore under its Academic Research Funding Tier

Studying and Assisting the Practice of Java and C# Exception Handling

Programming languages provide features that handle exceptions. These features separate error-handling from regular code and aim to assist software maintenance. Nevertheless, their misuse can cause reliability degradation or even catastrophic failures. Prior studies on exception handling aim to understand the practices of exception handling and their anti-patterns. However, little knowledge was shared about the prevalence of these anti-patterns, nor the relationship between exception handling practices and software quality. In this thesis, I, first, study the exception handling features by enriching the knowledge of handling code with a flow analysis of exceptions. Second, I investigate the prevalence of exception handling anti-patterns. Finally, I investigate the relationship between software quality and: (i) flow characteristics and (ii) 17 handling anti-patterns. Our case study is conducted with over 10K handling blocks, and over 77K related flows from 16 Java and C# projects. I built statistical models of the chance of post-release defects using traditional software metrics and exception handling metrics. Our case study results show the complexity of exception handling. Moreover, I found that although exception handling anti-patterns widely exist in all of our subjects, only a few anti-patterns can be commonly identified. Finally, I conclude that exception flow characteristics in Java projects and some exception handling anti-patterns can provide significant explanatory power to the chance of post-release defects