Supporting developers with natural language queries
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This document discusses using natural language queries to support software developers. It describes a system called Evolizer that uses several layers: 1) a persistence layer to store code data, 2) a data layer that models code using FAMIX, 3) an ontology layer that represents code artifacts semantically using RDF and OWL, and 4) a query layer called GINSENG that processes natural language queries and returns answers by executing SPARQL queries over the semantic model. The paper claims this approach allows developers to query code repositories without technical knowledge and without restrictions on the types of queries.
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Semantic webHon Lasisi H The document discusses the Semantic Web, which aims to develop the current web so that machines can understand the meaning of information and not just display it. It outlines some key technologies being used like XML, RDF, and ontologies to add structure and meaning to web content. This will allow software agents to perform more sophisticated tasks by processing structured, machine-readable information based on defined ontologies. The Semantic Web represents an evolution from today's web designed primarily for humans to one where machines can also comprehend and utilize web content.
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Doctoral Symposium of Masud RahmanMasud Rahman This document summarizes a talk given by Masud Rahman, a PhD candidate at the University of Saskatchewan. The talk focused on Rahman's PhD thesis research, which aims to improve code search by generating context-aware, analytics-driven queries through effective reformulation. The talk outlined three research questions around improving keyword selection, incorporating bug report quality, and using crowd knowledge and data analytics. It provided an overview of Rahman's PhD thesis and publications addressing the research questions. Evaluation methods for the proposed approaches were also discussed.
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MSR2017-ChallengeMasud Rahman This document summarizes research into the impact of continuous integration (CI) on code reviews. The researchers studied over 500,000 pull requests and builds from open source projects to answer three questions: 1) Whether build status influences code review participation, 2) If frequent builds improve review quality, and 3) Predicting if a build will trigger new reviews. They found that passed builds were more associated with new reviews and comments. Projects with frequent builds received more review comments that remained steady over time, unlike less frequently built projects. Their machine learning model could predict if a build would trigger new reviews with up to 64% accuracy.
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MSR2017-RevHelperMasud Rahman This document presents research on predicting the usefulness of code review comments using textual features and developer experience. The researchers analyzed 1,482 code review comments, manually classified as useful or non-useful. They found non-useful comments had more stop words and less code elements, while useful comments had higher conceptual similarity to changed code. More experienced reviewers provided more useful comments. The researchers also built a Random Forest model that predicts comment usefulness with 66% accuracy, outperforming baselines. Their work provides the first automated approach to assess code review comment usefulness.
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STRICT-SANER2017Masud Rahman The document describes a technique called STRICT that uses TextRank and POSRank algorithms to identify important terms from a software change task description to generate an effective initial search query. An experiment on 1,939 change tasks from 8 open source projects found that STRICT improved the query effectiveness in 57.84% of cases compared to baseline queries like title alone. STRICT also showed better retrieval performance based on metrics like mean average precision and mean recall compared to state-of-the-art techniques. The approach validates the use of graph-based ranking algorithms to address the challenge of generating relevant initial search queries from natural language change task descriptions.
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MSR2015-ChallengeMasud Rahman The document analyzes why some questions on Stack Overflow remain unresolved and explores whether machine learning can predict which questions will be unresolved. It finds that unresolved questions have higher topic entropy, meaning they are less specific. Owners of unresolved questions reject answers more often, have lower reputation, and are less active on Stack Overflow. Models using features like topic entropy, answer rejection ratio, and owner reputation achieved up to 78% accuracy at predicting unresolved questions. The study aims to help improve question quality on Stack Overflow.
MSR2014-Challenge
MSR2014-ChallengeMasud Rahman This document analyzes data from over 78,000 pull requests on GitHub to understand why pull request failure rates are high. It finds that 57.05% of pull requests failed, most often due to issues with recursion/refactoring, database queries, arrays/functions. Programming languages like Java, JavaScript and Ruby saw more failed pull requests on average than PHP. Projects in IDE and framework domains had the most pull request activity. Older projects, projects with more forks/developers, and projects where developers had 20-50 months of experience saw the highest numbers of pull requests and failures. The study aims to help understand and address common reasons for pull request failures on GitHub.
CodeInsight-SCAM2015
CodeInsight-SCAM2015Masud Rahman The document describes a technique called CodeInsight that mines insightful code comments from crowdsourced knowledge on Stack Overflow. An exploratory study of Stack Overflow discussions found that around 22% of comments discuss tips, bugs, or warnings related to code examples. CodeInsight uses heuristics like popularity, relevance, comment rank, sentiment, and word count to retrieve these insightful comments for a given code segment. An empirical evaluation showed the technique could recall over 80% of relevant comments on average. A user study with professional developers found that 80% of the comments recommended by CodeInsight were accurate and useful.
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STRICT-SANER2015Masud Rahman This document proposes using TextRank to identify initial search terms for software change tasks. It adapts TextRank, originally used for keyword extraction and text summarization, to build a graph of terms from development artifacts and rank them. An evaluation on 349 change tasks from two systems identifies search terms, which outperform an existing approach in solving more tasks with higher precision and recall. The approach recommends initial search queries to help developers find relevant code artifacts when performing change tasks.
CMPT-842-BRACK
CMPT-842-BRACKMasud Rahman This document discusses a method called BRACK for identifying bug-prone API methods using crowdsourced knowledge from Stack Overflow. BRACK ranks API method invocations based on two heuristics: API Context-Susceptibility (ACS) which estimates how context can impact an invocation, and API Error-Associativity (AEA) which calculates the co-occurrence of an invocation in defective and corrected code segments. An evaluation of BRACK on 8 open source systems found that it achieved a top-3 accuracy of 75.93% in identifying bug-prone invocations, and that ACS was more effective than AEA. The evaluation also showed BRACK had no significant bias towards system size or API package and performed comparably
RACK-Tool-ICSE2017
RACK-Tool-ICSE2017Masud Rahman The document presents research on RACK, a tool that uses crowdsourced knowledge from Stack Overflow to reformulate natural language code search queries into relevant API names. The researchers analyzed Stack Overflow data to find that answers frequently refer to APIs by name and cover a high percentage of core APIs. They also found question titles contain terms relevant to real code search queries. RACK maps query terms to API names using this data, then searches GitHub code examples. An evaluation showed RACK returns relevant examples with 79% top-10 accuracy, outperforming existing techniques.
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RACK-SANER2016Masud Rahman RACK is an approach that automatically recommends relevant APIs for code search queries using crowdsourced knowledge from Stack Overflow questions, answers, and titles. An exploratory study found that accepted Stack Overflow answers frequently mention API names and cover a large percentage of standard APIs. Question titles often contain keywords relevant to code search. RACK constructs an API-token mapping database from Stack Overflow and ranks APIs for a given query based on heuristics measuring keyword-API co-occurrence and coherence. An evaluation found RACK achieved around 79% top-10 accuracy and outperformed existing techniques, demonstrating the potential of leveraging crowdsourced technical knowledge for API recommendation.
QUICKAR-ASE2016-Singapore
QUICKAR-ASE2016-SingaporeMasud Rahman QUICKAR is a technique for automatically reformulating code search queries using crowdsourced knowledge from Stack Overflow. It constructs an adjacency list database of terms from Stack Overflow question titles. For an initial search query, it identifies reformulation candidates by comparing the query terms to terms in the adjacency list database and project source code. In experiments, QUICKAR significantly outperformed a baseline technique, improving over 50% of queries while worsening less than 50%, by leveraging vocabulary from Stack Overflow to address mismatches between developer queries and code.
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CORRECT-ToolDemo-ASE2016Masud Rahman CORRECT is a code reviewer recommendation tool that:
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Supporting developers with natural language queries
- 1. Supporting Developers with Natural Language Queries Michael Würsch, Giacomo Ghezzi, Gerald Reif & Harald C.Gall Presented By: Mohammad Masudur Rahman
- 2. Contents Overview Semantic Web Layers of Evolizer Working Procedures Claims of Paper My Observation / Future Works
- 3. Introduction Modern IDE can’t answer all questions Natural language is best way to question Data Extracted from code repositories Data Modeled using OWL and Semantic Web GINSENG to process NL query Evolizer to produce results
- 4. Semantic Web (RDF) Envisioned by Tim Berners-Lee, Web 3.0 An extension of the Web in which information is given well defined meaning, better enabling computers and people to work in cooperation. Everything having a URI is a resource RDF statement has 3 parts – subject, predicate and object Subject-predicate-object triple as RDF stmt. The director of Titanic is James Cameron
- 5. Semantic Web (RDF) <?xml version="1.0"?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:mov="http://www.recshop.fake/mov#"> <rdf:Description rdf:about="http://www.recshop.fake/mov/Titanic"> <mov:director>James Cameron</mov:director> <mov:hero>Leonardo De Caprio</mov:hero> <mov:heroine>Kate Winslate</mov:heroine> </rdf:Description> </rdf:RDF>
- 7. Layers of Evolizer Persistency layer (Hibernate, RHDB) Data layer (FAMIX) Ontology layer (RDF, OWL) Query layer (GINSENG)
- 8. I. Persistency Layer EVOLIZER (framework) CHANGEDISTILLER, based on Evolizer considers RHDB DA4JAVA based on current code
- 9. II. Data Layer FAMIX Model of OO Source code by ZBinder Language independent
- 10. III. Ontology Layer @rdf annotation of java, OWL
- 11. III. Ontology Layer Ontology Graph
- 12. IV. Query Layer Guided Input Natural Language Search Engine - GINSENG Natural Language What are the methods of DefaultTitleEditor? SPARQL Query
- 13. Paper’s Claims Guided Input Natural language support for query answering No knowledge needed for querying No restriction on querying, no fixed questions Efficient use of semantic web to represent code artifacts Both use of RHDB and current code
- 14. My Observations (Future Works) Performance evaluation factors not properly specified Experienced developers may prefer keywords to a complete NL sentence for query Novice developers would prefer suggestion from system to asking question by themselves NL query through voice can make the system more usable and helpful Software can track novice developer’s movement over the code and suggest helpful hints for understanding