An important goal for programmers is to minimize cost of identifying and correcting defects in source code. Code review is commonly used for identifying programming defects. However, manual code review has some shortcomings: a) it is time consuming, b) outcomes are subjective and depend on the skills of reviewers. An automated approach for assisting in code reviews is thus highly desirable. We present a tool for assisting in code review and results from our experiments evaluating the tool in different scenarios. The tool leveraged content available from professional programmer support forums (e.g. StackOverflow.com) to determine potential defectiveness of a given piece of source code. The defectiveness is expressed on the scale of Likely defective, Neutral, Unlikely to be defective . Basic idea employed in the tool is to: a) Identify a set P of discussion posts on StackOverflow such that each p in P contains source code fragment(s) which sufficiently resemble the input code C being reviewed. b) Determine the likelihood of C being defective by considering all p in P . A novel aspect of our approach is to use document fingerprinting for comparing two pieces of source code. Our choice of document fingerprinting technique is inspired by source code plagiarism detection tools where it has proven to be very successful. In the experiments that we performed to verify effectiveness of our approach source code samples from more than 300 GitHub open source repositories were taken as input. A precision of more than 90 in identifying correct relevant results has been achieved.
Similarly, @cite_14 proposes a . They implemented an IDE (Eclipse) plug-in that makes use of the APIs provided by popular web search engines (viz. Google, Yahoo, Bing) to search the entire Web using keywords from a programming context in an IDE. They determine relevant results by taking into consideration the programming context as well as popularity of the candidate search results.
@cite_14
2016035035 2051074879 1974020522 1989357464
Study shows that software developers spend about 19 of their time looking for information in the web during software development and maintenance. Traditional web search forces them to leave the working environment (e.g., IDE) and look for information in the web browser. It also does not consider the context of the problems that the developers search solutions for. The frequent switching between web browser and the IDE is both time-consuming and distracting, and the keyword-based traditional web search often does not help much in problem solving. In this paper, we propose an Eclipse IDE-based web search solution that exploits the APIs provided by three popular web search engines-Google, Yahoo, Bing and a popular programming Q & A site, StackOverflow, and captures the content-relevance, context-relevance, popularity and search engine confidence of each candidate result against the encountered programming problems. Experiments with 75 programming errors and exceptions using the proposed approach show that inclusion of different types of contextual information associated with a given exception can enhance the recommendation accuracy of a given exception. Experiments both with two existing approaches and existing web search engines confirm that our approach can perform better than them in terms of recall, mean precision and other performance measures with little computational cost. When faced with the need for documentation, examples, bug fixes, error descriptions, code snippets, workarounds, templates, patterns, or advice, software developers frequently turn to their web browser. Web resources both organized and authoritative as well as informal and community-driven are heavily used by developers. The time and attention devoted to finding (or re-finding) and navigating these sites is significant. We present Codetrail, a system that demonstrates how the developer's use of web resources can be improved by connecting the Eclipse integrated development environment (IDE) and the Firefox web browser. Codetrail uses a communication channel and shared data model between these applications to implement a variety of integrative tools. By combining information previously available only to the IDE or the web browser alone (such as editing history, code contents, and recent browsing), Codetrail can automate previously manual tasks and enable new interactions that exploit the marriage of data and functionality from Firefox and Eclipse. Just as the IDE will change the contents of peripheral views to focus on the particular code or task with which the developer is engaged, so, too, the web browser can be focused on the developer's current context and task. Nearly every Integrated Development Environment includes a form of code completion. The suggested completions ("suggestions") are typically based on information available at compile time, such as type signatures and variables in scope. A statistical approach, based on estimated models of code patterns in large code corpora, has been demonstrated to be effective at predicting tokens given a context. In this demo, we present CACHECA, an Eclipse plug in that combines the native suggestions with a statistical suggestion regime. We demonstrate that a combination of the two approaches more than doubles Eclipse's suggestion accuracy. A video demonstration is available at https: www.youtube.com watch?v=3INk0N3JNtc. Modern IDEs make many software engineering tasks easier by automating functionality such as code completion and navigation. However, this functionality operates on one version of the code at a time. We envision a new approach that makes code completion and navigation aware of code evolution and enables them to operate on multiple versions at a time, without having to manually switch across these versions. We illustrate our approach on several example scenarios. We also describe a prototype Eclipse plugin that embodies our approach for code completion and navigation for Java code. We believe our approach opens a new line of research that adds a novel, temporal dimension for treating code in IDEs in the context of tasks that previously required manual switching across different code versions.