Supporting program comprehension with source code summarization
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This document discusses research on automatically generating summaries of source code to help with program comprehension. It proposes using techniques like latent semantic indexing to extract important lexical and structural information from code, and generate summaries at different granularity levels like class or method. Experiments on an open source project showed that incorporating structural elements like method names into automatic summaries improved their quality compared to only using term frequencies. Future work could develop better techniques to account for structural information when creating code summaries.
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HereWeCode 2022: Dalhousie University
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The Forgotten Role of Search Queries in IR-based Bug Localization: An Empiric...Masud Rahman The document summarizes a study on improving search queries for bug localization using natural language text from bug reports. The study evaluated different keyword selection techniques, generated optimal search queries using a genetic algorithm, and compared optimal versus non-optimal queries. Key findings include: 1) Current approaches failed to identify keywords for 34% of bug reports, 2) A genetic algorithm produced optimal queries that achieved up to 80% higher performance than baselines, and 3) Optimal queries differed in using less frequent, less ambiguous, noun-heavy keywords located in bug report bodies.
PhD Seminar - Masud Rahman, University of Saskatchewan
PhD Seminar - Masud Rahman, University of SaskatchewanMasud Rahman Masud Rahman's PhD thesis focuses on enhancing source code search through context-aware and semantics-driven query reformulation, addressing issues such as bug localization and concept location. The research includes systematic literature reviews and experiments on various query reformulation techniques. Future works involve exploring quality-aware refactoring for bug reports and improving internet-scale code search using crowdsourced knowledge.
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PhD proposal of Masud RahmanMasud Rahman The document outlines Masud Rahman's PhD thesis proposal on supporting source code search with context-aware, analytics-driven query reformulation. The proposal discusses three research questions: 1) evaluating term weighting techniques for keyword selection from source code and bug reports, 2) incorporating bug report quality for local code search, and 3) leveraging crowd knowledge and data analytics to deliver query keywords. The contribution summary highlights techniques for term dependence, quality-aware bug localization, and using crowd knowledge and large data analytics.
PhD Comprehensive exam of Masud Rahman
PhD Comprehensive exam of Masud RahmanMasud Rahman This document presents a systematic literature review of automated query reformulations for source code search. It discusses seven research questions explored in the review, including the methods, algorithms, data sources, evaluation metrics, challenges, publication trends, and comparisons between local and internet-scale code search queries. The review analyzed over 50 primary studies identified through a multi-database search and filtering process. Key findings include the predominant use of term weighting, query expansion and reduction techniques, evaluations based on standard information retrieval metrics, and various challenges like vocabulary mismatch that remain unsolved. Opportunities for future work are also identified, such as leveraging bug reports for keyword selection and using semantic representations to address vocabulary issues.
Doctoral Symposium of Masud Rahman
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.
Supporting Source Code Search with Context-Aware and Semantics-Driven Code Se...
Supporting Source Code Search with Context-Aware and Semantics-Driven Code Se...Masud Rahman The document outlines a PhD thesis presentation by Masud Rahman focusing on enhancing source code search through context-aware and semantics-driven query reformulation. It details various aspects such as bug localization, concept location, and internet-scale code search, along with systematic literature reviews and comparisons with existing methodologies. The presentation includes a demo titled 'Bug Doctor' and concludes with a Q&A section.
ICSE2018-Poster-Bug-Localization
ICSE2018-Poster-Bug-LocalizationMasud Rahman This document summarizes a study on improving bug localization through considering the quality of bug reports and reformulating bug report queries. The study analyzes 5,500 bug reports from eight projects and finds that existing bug localization techniques perform poorly when bug reports lack useful information or contain excessive stack traces. Preliminary findings suggest context-aware query reformulation may help address these limitations by improving the quality and relevance of the queries used.
MSR2017-Challenge
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.
MSR2017-RevHelper
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.
STRICT-SANER2017
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.
MSR2015-Challenge
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.
STRICT-SANER2015
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.
RACK-SANER2016
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.
CORRECT-ToolDemo-ASE2016
CORRECT-ToolDemo-ASE2016Masud Rahman CORRECT is a code reviewer recommendation tool that:
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- Provides recommendation rationales that fit within developers' workflows
- Achieves over 90% accuracy in recommending reviewers based on library and technology experience
- Outperforms an existing technique (RevFinder) with 92.15% top-5 accuracy, 85.93% mean precision and 81.39% mean recall
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Supporting program comprehension with source code summarization
- 1. Supporting Program Comprehension with Source Code Summarization Sonia Haiduc, Jairo Aponte, Andrian Marcus Presented By: Mohammad Masudur Rahman
- 2. Contents 2 Why Code Summarization? Thesis Statement Research Questions about summary Research Questions about tool Automatic Code Summarization Evaluation Experiments Conducted Pyramid Method Important Findings My Observation & Future Works
- 3. Why Code Summarization? Program comprehension 50% of all maintenance works Two extreme approaches – skim through and read thoroughly Skim through – leads to misunderstanding Read thoroughly – time consuming An intermediate solution – source code entity with comprehensive textual description 3
- 4. Thesis Statement New idea: code summarization to help in program comprehension (PC) Applying TR methods like Latent Semantic Indexing in source code summarization. Combining structural information with retrieved code summary to make it effective for realistic purposes. 4
- 5. Research Questions of Code Summarization Summary should be automatically generated Generate summary to different granularity levels – class, method, packages etc Shorter than the source code Capture and preserve code semantics and structure – text as well as structure from the code Consistent structure – important items at first 5
- 6. Research Questions of Code Summarization Summary should reflect the developer’s understanding about the code Tool should allow user to change summary and will remember user’s choice in future summary Tool should rebuild the summary if the code changes or developer’s provide feedback 6
- 7. Research Questions about Summarizer Tool 7 Which summarization technique works the best for source code? What type of structural info necessary in summary? Will the summary be different for different type of maintenance task? How long it would be? How much will it resemble to actual summary? How do developers generate summary?
- 8. Automatic Code Summarization Generate extractive summary – the most important info extracted from the document 8
- 9. Automatic Code Summarization Two types info extracted – lexical and structural Lexical info – identifiers and comments are extracted Common English and PL keywords are removed Identifiers are split into constituent words and stemming performed. 9
- 10. Automatic Code Summarization Extracted lexical info forms the text corpus of code where TR methods (e.g. LSI) used to get most important n words. Once retrieved, n words are combined with structural info like their class name, method name, package name, parameter name and type etc How to apply structural info to autogenerated summary is an important part 10
- 11. Automatic Code Summarization A method name reflects the description of what it does. If method name ignored by TR, the tool can introduce it automatically Additional info can be added like –user tags 11
- 12. Evaluation 12 Two types – intrinsic and extrinsic Intrinsic – content evaluation, how closely it depicts the document or how close to manually generated summary Metrics- precision, recall, pyramid method Extrinsic – how much utility and usability it has to support SE tasks – concept location, impact analysis, software reuse, traceability links recovery etc
- 13. Experiments Conducted Pyramid method ATunes OS project, 12 methods 6 developers from different demographic locations, undergraduate students, 3 years Java programming experiences Developers provided with a list of terms, they need to choose 5 terms for each method that suits best, 60 minutes total time 13
- 14. Experiments Conducted Corpus containing whole code vocabulary Each method is a different document LSI indexing the corpus against each method terms Cosine measure between corpus and method and corpus words are ranked Top 5 words from corpus are chosen 14
- 15. Pyramid method Pyramid score = (Sum of A’s score / Total score A could make) 15
- 16. Pyramid Score 16
- 17. Important Findings 17 Pyramid score >=.1 and <=.5, marked it encouraging Words chosen by developers – 98.7% in method name, 88.9% in class name and 84.6% in parameter name Automatic summary terms – 20% in method name, 12.9% in class name and 30.7% in parameter name Structural info should be considered properly in automatic summary Comments text not included in summary
- 18. My Observation &Future Works 18 The corpus development technique is not well specified- no specification about redundancy protection LSI focuses on term frequency rather than structural info which produces bad scores. During cosine measurement structural info of term in the method could be considered to get better results There should have some heuristic measurement for structural info.