Presented at: The 6th International Workshop on Refactoring (IWoR 2022)
Date of Workshop: 14 October 2022
Conference Location: Oakland Center, Michigan, USA
This document contains the resume of Akash Mishra. It summarizes his career objective, work experience, technical skills, education, and certifications. He has over 6 years of experience in IT as a storage administrator. He is proficient in administering HP 3PAR, IBM DS5100, and Hitachi VSP storage arrays. He also has experience with SAN switches, IBM TSM backup, tape libraries, Windows and Linux servers. He holds certifications like MCSE, ITIL Foundation, and has attended training on SAN storage technologies. His most recent role was as a Storage and Backup Administrator with Tata Consultancy Services since 2014 at the Rajasthan State Data Centre.
Percona XtraDB Cluster vs Galera Cluster vs MySQL Group ReplicationKenny Gryp
This document provides an overview of different database replication technologies including Galera Cluster, Percona XtraDB Cluster, and MySQL Group Replication. It discusses similarities between the technologies such as multi-master replication topologies and consistency models. Key differences are also outlined relating to node provisioning, failure handling, and operational limitations of each solution. Known issues uncovered through quality assurance testing are also briefly mentioned.
Ip sec vpn with dynamic routing mikrotik and cisco - mikro-tik wikiHuy Eav
This document summarizes how to set up an IPSec VPN between a Mikrotik router and Cisco router using dynamic routing protocol RIP. It provides configuration details for establishing an IPsec tunnel interface and enabling RIP routing to exchange routes between the Mikrotik and Cisco routers over the encrypted tunnel.
Configurar firewall de windows para permitir el acceso a sql server segundo p...Jesus Garcia Guevara
Este documento explica cómo configurar el Firewall de Windows para permitir el acceso a SQL Server. Detalla los puertos utilizados por SQL Server y los programas disponibles para configurar el firewall, como el elemento Firewall de Windows en el Panel de control y el complemento MMC de Firewall de Windows con seguridad avanzada. También cubre conceptos como puertos dinámicos y fijos, y proporciona instrucciones paso a paso para configurar las reglas del firewall.
DNS (Domain Name System) is a hierarchical naming system that translates domain names to IP addresses and vice versa. A DNS server is a computer that runs DNS services to provide name resolution. DNS works by querying multiple levels of DNS servers, starting from the root servers, then TLD (top-level domain) servers, then authoritative name servers, to ultimately resolve domain names to IP addresses.
Ph.D. Dissertation Presentation
B. Thomas Golisano College of Computing and Information Sciences
Rochester Institute of Technology
Date of presentation: June 28, 2022
Location: Virtual
Link to dissertation: https://scholarworks.rit.edu/theses/11219/
Presented at: The 44th IEEE/ACM International Conference on Software Engineering (ICSE 2022)
Date of Conference: May 2022
Conference Location: Virtual & Pittsburgh, PA, USA
This paper was originally published in the Empirical Software Engineering journal
The preprint is available at: https://arxiv.org/pdf/2110.12229
A video of the presentation is available at: https://youtu.be/suWRL2nmxMs
Laleh M. Eshkevari defended her Ph.D dissertation on developing techniques for the automatic detection and classification of identifier renamings in software projects. Her dissertation outlined a taxonomy of renamings, described approaches for renaming detection based on line mapping, entity mapping and data flow analysis, and discussed methods for classifying renamings based on their form and semantic changes. Evaluation of the approaches on several open source projects showed high precision and recall for renaming detection and identified trends in how renamings are used in practice.
Presented at: 19th IEEE International Working Conference on Source Code Analysis and Manipulation
Date of Conference: 30 Sept.-1 Oct. 2019
Conference Location: Cleveland, OH, USA
DOI: https://doi.org/10.1109/SCAM.2019.00017
This document provides an agenda for a tutorial on candidate selection techniques for large scale personalized search and recommender systems. The tutorial will cover the lifetime of a query, indexing building, query understanding, and candidate selection and retrieval. It will also include a case study on LinkedIn job search and recommendations. Attendees will learn about building blocks of large scale search systems, query processing, candidate selection techniques, and build a prototype search system. The result will be a full stack search system on a news dataset using open source tools.
SIGIR 2017 - Candidate Selection for Large Scale Personalized Search and Reco...Aman Grover
This document provides an agenda for a tutorial on candidate selection techniques for large scale personalized search and recommender systems. The tutorial will cover the lifetime of a query, indexing building, query understanding, and candidate selection and retrieval. It will also include a case study on LinkedIn job search and recommendations. Attendees will learn about building blocks of large scale search systems, query processing, candidate selection techniques, and build a prototype search system. The result will be a full stack search system on a news dataset using open source tools.
Recommending refactoring operations in large software systemsCarlos Eduardo
The document discusses algorithms for recommending refactoring operations in large software systems. It describes two main algorithms - a clustering-based algorithm and a graph-based algorithm. The clustering-based algorithm identifies groups of similar methods and entities that could be extracted into separate classes. The graph-based algorithm builds a matrix of relationships between methods and identifies strongly related chains of methods that could form candidate classes. Both algorithms were evaluated on open source systems and shown to effectively recommend extract class refactoring opportunities.
Keyphrase Extraction And Source Code Similarity Detection- A Survey Nakul Sharma
This is the presentation given at chsn2020. For full article please visit the website:https://iopscience.iop.org/article/10.1088/1757-899X/1074/1/012027 or https://doi.org/10.1088/1757-899X/1074/1/012027
This document discusses refactoring and metaprogramming. It provides an overview of topics including refactoring basics, refactoring tools in Squeak, and the implementation of the refactoring engine. The refactoring engine uses an abstract syntax tree to represent code and tree rewriting to specify transformations. Reflection is discussed, noting that while refactoring changes a system using itself, the refactoring engine builds its own abstraction layer rather than using the system's reflective capabilities.
Following are the questions which I tried to answer in this ppt
What is text summarization.
What is automatic text summarization?
How it has evolved over the time?
What are different methods?
How deep learning is used for text summarization?
business application
in first few slides extractive summarization is explained, with pro and cons in next section abstractive on is explained.
In the last section business application of each one is highlighted
Beyond Transparency: Success & Lessons From tambisBoston2003robertstevens65
TAMBIS (The Anthropic Mediated Bioinformatics Service) aims to provide a single query language, data model, and location for distributed biological information sources by creating the illusion of transparency. It does this through ontologies that provide a consistent shared understanding of metadata, and middleware that rewrites user queries against the ontology into coordinated multi-source requests. While the illusion of transparency is appealing, it requires significant effort to maintain and does not accommodate the changing nature of sources. The greatest outcomes were found to be the ontologies and knowledge representation techniques developed for the system.
Webinar: Question Answering and Virtual Assistants with Deep LearningLucidworks
This document discusses question answering and virtual assistants using deep learning. It provides an overview of question answering systems, including their uses for customer support and knowledge transfer. It describes the typical workflow of initial candidate retrieval using Solr followed by reranking using machine learning models. The document also discusses feature engineering, training data sources, and models for question answering, including supervised models like XGBoost and Siamese neural networks as well as unsupervised models using embeddings. It concludes that deep learning models outperform traditional models with sufficient training data.
Innovating Multi-Class Text Classification:Transforming Models with propmtify...ankarao14
This document discusses using Promptify, a method for generating prompts to guide language model behavior, for multi-class text classification. It begins with an abstract describing how text classification has evolved over time and the objectives of exploring Promptify for this task. The introduction provides background on text classification and motivation for exploring Promptify. It then outlines the contents which will include literature review on text classification techniques, Promptify applications, methodology, implementation, and evaluation results.
Innovating Multi-Class Text Classification:Transforming Models with propmtify...ankarao14
This document discusses using Promptify, a method for generating prompts to guide language model behavior, for multi-class text classification. It begins with an abstract describing how text classification has evolved over time and the objectives of exploring Promptify for this task. The introduction provides background on text classification and motivation for exploring Promptify. It then outlines the contents which will include literature review on text classification techniques, Promptify applications, methodology, implementation, and evaluation results.
Natural Language Processing Advancements By Deep Learning: A SurveyRimzim Thube
This document provides an overview of advancements in natural language processing through deep learning techniques. It describes several deep learning architectures used for NLP tasks, including multi-layer perceptrons, convolutional neural networks, recurrent neural networks, auto-encoders, and generative adversarial networks. It also summarizes applications of these techniques to common NLP problems such as part-of-speech tagging, parsing, named entity recognition, sentiment analysis, machine translation, question answering, and text summarization.
The recommendations system for source code components retrievalAYESHA JAVED
This document proposes a recommender system to help software developers find reusable source code components. It discusses issues with current code search tools and outlines objectives to develop a system that extracts source code, documentation and files from repositories to effectively rank and retrieve relevant code based on queries. The proposed solution would calculate component functional and reusability scores to recommend source code snippets that best match requirements and queries. The system aims to ease the task of code retrieval and integration for developers.
52 - The Impact of Test Ownership and Team Structure on the Reliability and E...ESEM 2014
Context: Software testing is a crucial step in most software development processes. Testing software is a key component to manage and assess the risk of shipping quality products to customers. But testing is also an expensive process and changes to the system need to be tested thoroughly which may take time. Thus, the quality of a software product depends on the quality of its underlying testing process and on the effectiveness and reliability of individual test cases.
Goal: In this paper, we investigate the impact of the organizational structure of test owners on the reliability and effectiveness of the corresponding test cases. Prior empirical research on organizational structure has focused only on developer activity. We expand the scope of empirical knowledge by assessing the impact of organizational structure on testing activities.
Method: We performed an empirical study on the Windows build verification test suites (BVT) and relate effectiveness and reliability measures of each test run to the complexity and size of the organizational sub-structure that enclose all owners of test cases executed.
Results: Our results show, that organizational structure impacts both test effectiveness and test execution reliability. We are also able to predict effectiveness and reliability with fairly high precision and recall values.
Conclusion: We suggest to review test suites with respect to their organizational composition. As indicated by the results of this study, this would increase the effectiveness and reliability, development speed and developer satisfaction.
This presentation explores code comprehension challenges in scientific programming based on a survey of 57 research scientists. It reveals that 57.9% of scientists have no formal training in writing readable code. Key findings highlight a "documentation paradox" where documentation is both the most common readability practice and the biggest challenge scientists face. The study identifies critical issues with naming conventions and code organization, noting that 100% of scientists agree readable code is essential for reproducible research. The research concludes with four key recommendations: expanding programming education for scientists, conducting targeted research on scientific code quality, developing specialized tools, and establishing clearer documentation guidelines for scientific software.
Presented at: The 33rd International Conference on Program Comprehension (ICPC '25)
Date of Conference: April 2025
Conference Location: Ottawa, Ontario, Canada
Preprint: https://arxiv.org/abs/2501.10037
This presentation examines accessibility trends and challenges in mobile app development through an analysis of Stack Overflow questions. The researchers analyzed 3,022 questions over a 15-year period, focusing on three key areas: growth patterns, question characteristics, and development challenges. The study revealed that accessibility-related questions peaked in 2016 with 305 questions. Question engagement metrics showed strong community participation, with 73% of questions receiving answers and a median response time of 1.3 days. Through topic modeling and manual review, seven major challenge areas were identified that developers face: screen readers and navigation, UI elements and interactions, touch gestures, multilingual support, dynamic text handling, custom Android accessibility services, and testing/automation.
Presented at: The 58th Hawaii International Conference on System Sciences (HICSS '25)
Date of Conference: Jan 2025
Conference Location: Waikoloa, Hawaii, United States
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Laleh M. Eshkevari defended her Ph.D dissertation on developing techniques for the automatic detection and classification of identifier renamings in software projects. Her dissertation outlined a taxonomy of renamings, described approaches for renaming detection based on line mapping, entity mapping and data flow analysis, and discussed methods for classifying renamings based on their form and semantic changes. Evaluation of the approaches on several open source projects showed high precision and recall for renaming detection and identified trends in how renamings are used in practice.
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Date of Conference: 30 Sept.-1 Oct. 2019
Conference Location: Cleveland, OH, USA
DOI: https://doi.org/10.1109/SCAM.2019.00017
This document provides an agenda for a tutorial on candidate selection techniques for large scale personalized search and recommender systems. The tutorial will cover the lifetime of a query, indexing building, query understanding, and candidate selection and retrieval. It will also include a case study on LinkedIn job search and recommendations. Attendees will learn about building blocks of large scale search systems, query processing, candidate selection techniques, and build a prototype search system. The result will be a full stack search system on a news dataset using open source tools.
SIGIR 2017 - Candidate Selection for Large Scale Personalized Search and Reco...Aman Grover
This document provides an agenda for a tutorial on candidate selection techniques for large scale personalized search and recommender systems. The tutorial will cover the lifetime of a query, indexing building, query understanding, and candidate selection and retrieval. It will also include a case study on LinkedIn job search and recommendations. Attendees will learn about building blocks of large scale search systems, query processing, candidate selection techniques, and build a prototype search system. The result will be a full stack search system on a news dataset using open source tools.
Recommending refactoring operations in large software systemsCarlos Eduardo
The document discusses algorithms for recommending refactoring operations in large software systems. It describes two main algorithms - a clustering-based algorithm and a graph-based algorithm. The clustering-based algorithm identifies groups of similar methods and entities that could be extracted into separate classes. The graph-based algorithm builds a matrix of relationships between methods and identifies strongly related chains of methods that could form candidate classes. Both algorithms were evaluated on open source systems and shown to effectively recommend extract class refactoring opportunities.
Keyphrase Extraction And Source Code Similarity Detection- A Survey Nakul Sharma
This is the presentation given at chsn2020. For full article please visit the website:https://iopscience.iop.org/article/10.1088/1757-899X/1074/1/012027 or https://doi.org/10.1088/1757-899X/1074/1/012027
This document discusses refactoring and metaprogramming. It provides an overview of topics including refactoring basics, refactoring tools in Squeak, and the implementation of the refactoring engine. The refactoring engine uses an abstract syntax tree to represent code and tree rewriting to specify transformations. Reflection is discussed, noting that while refactoring changes a system using itself, the refactoring engine builds its own abstraction layer rather than using the system's reflective capabilities.
Following are the questions which I tried to answer in this ppt
What is text summarization.
What is automatic text summarization?
How it has evolved over the time?
What are different methods?
How deep learning is used for text summarization?
business application
in first few slides extractive summarization is explained, with pro and cons in next section abstractive on is explained.
In the last section business application of each one is highlighted
Beyond Transparency: Success & Lessons From tambisBoston2003robertstevens65
TAMBIS (The Anthropic Mediated Bioinformatics Service) aims to provide a single query language, data model, and location for distributed biological information sources by creating the illusion of transparency. It does this through ontologies that provide a consistent shared understanding of metadata, and middleware that rewrites user queries against the ontology into coordinated multi-source requests. While the illusion of transparency is appealing, it requires significant effort to maintain and does not accommodate the changing nature of sources. The greatest outcomes were found to be the ontologies and knowledge representation techniques developed for the system.
Webinar: Question Answering and Virtual Assistants with Deep LearningLucidworks
This document discusses question answering and virtual assistants using deep learning. It provides an overview of question answering systems, including their uses for customer support and knowledge transfer. It describes the typical workflow of initial candidate retrieval using Solr followed by reranking using machine learning models. The document also discusses feature engineering, training data sources, and models for question answering, including supervised models like XGBoost and Siamese neural networks as well as unsupervised models using embeddings. It concludes that deep learning models outperform traditional models with sufficient training data.
Innovating Multi-Class Text Classification:Transforming Models with propmtify...ankarao14
This document discusses using Promptify, a method for generating prompts to guide language model behavior, for multi-class text classification. It begins with an abstract describing how text classification has evolved over time and the objectives of exploring Promptify for this task. The introduction provides background on text classification and motivation for exploring Promptify. It then outlines the contents which will include literature review on text classification techniques, Promptify applications, methodology, implementation, and evaluation results.
Innovating Multi-Class Text Classification:Transforming Models with propmtify...ankarao14
This document discusses using Promptify, a method for generating prompts to guide language model behavior, for multi-class text classification. It begins with an abstract describing how text classification has evolved over time and the objectives of exploring Promptify for this task. The introduction provides background on text classification and motivation for exploring Promptify. It then outlines the contents which will include literature review on text classification techniques, Promptify applications, methodology, implementation, and evaluation results.
Natural Language Processing Advancements By Deep Learning: A SurveyRimzim Thube
This document provides an overview of advancements in natural language processing through deep learning techniques. It describes several deep learning architectures used for NLP tasks, including multi-layer perceptrons, convolutional neural networks, recurrent neural networks, auto-encoders, and generative adversarial networks. It also summarizes applications of these techniques to common NLP problems such as part-of-speech tagging, parsing, named entity recognition, sentiment analysis, machine translation, question answering, and text summarization.
The recommendations system for source code components retrievalAYESHA JAVED
This document proposes a recommender system to help software developers find reusable source code components. It discusses issues with current code search tools and outlines objectives to develop a system that extracts source code, documentation and files from repositories to effectively rank and retrieve relevant code based on queries. The proposed solution would calculate component functional and reusability scores to recommend source code snippets that best match requirements and queries. The system aims to ease the task of code retrieval and integration for developers.
52 - The Impact of Test Ownership and Team Structure on the Reliability and E...ESEM 2014
Context: Software testing is a crucial step in most software development processes. Testing software is a key component to manage and assess the risk of shipping quality products to customers. But testing is also an expensive process and changes to the system need to be tested thoroughly which may take time. Thus, the quality of a software product depends on the quality of its underlying testing process and on the effectiveness and reliability of individual test cases.
Goal: In this paper, we investigate the impact of the organizational structure of test owners on the reliability and effectiveness of the corresponding test cases. Prior empirical research on organizational structure has focused only on developer activity. We expand the scope of empirical knowledge by assessing the impact of organizational structure on testing activities.
Method: We performed an empirical study on the Windows build verification test suites (BVT) and relate effectiveness and reliability measures of each test run to the complexity and size of the organizational sub-structure that enclose all owners of test cases executed.
Results: Our results show, that organizational structure impacts both test effectiveness and test execution reliability. We are also able to predict effectiveness and reliability with fairly high precision and recall values.
Conclusion: We suggest to review test suites with respect to their organizational composition. As indicated by the results of this study, this would increase the effectiveness and reliability, development speed and developer satisfaction.
This presentation explores code comprehension challenges in scientific programming based on a survey of 57 research scientists. It reveals that 57.9% of scientists have no formal training in writing readable code. Key findings highlight a "documentation paradox" where documentation is both the most common readability practice and the biggest challenge scientists face. The study identifies critical issues with naming conventions and code organization, noting that 100% of scientists agree readable code is essential for reproducible research. The research concludes with four key recommendations: expanding programming education for scientists, conducting targeted research on scientific code quality, developing specialized tools, and establishing clearer documentation guidelines for scientific software.
Presented at: The 33rd International Conference on Program Comprehension (ICPC '25)
Date of Conference: April 2025
Conference Location: Ottawa, Ontario, Canada
Preprint: https://arxiv.org/abs/2501.10037
This presentation examines accessibility trends and challenges in mobile app development through an analysis of Stack Overflow questions. The researchers analyzed 3,022 questions over a 15-year period, focusing on three key areas: growth patterns, question characteristics, and development challenges. The study revealed that accessibility-related questions peaked in 2016 with 305 questions. Question engagement metrics showed strong community participation, with 73% of questions receiving answers and a median response time of 1.3 days. Through topic modeling and manual review, seven major challenge areas were identified that developers face: screen readers and navigation, UI elements and interactions, touch gestures, multilingual support, dynamic text handling, custom Android accessibility services, and testing/automation.
Presented at: The 58th Hawaii International Conference on System Sciences (HICSS '25)
Date of Conference: Jan 2025
Conference Location: Waikoloa, Hawaii, United States
This presentation discusses how generative AI-powered code generation tools are impacting software engineer hiring practices. The researchers surveyed 32 industry professionals, primarily recruiters and hiring managers, at a Spring 2024 Career Fair to understand their experiences, perceptions, and strategies around evaluating candidates in an era of AI coding assistants. Key findings include that while ChatGPT is the most familiar AI tool among recruiters (60.87%), most organizations don't yet have formal policies about AI use in technical interviews. The study also found that recruiters value cognitive skills and prompt engineering abilities over just technical proficiency.
Presented at: The 58th Hawaii International Conference on System Sciences (HICSS '25)
Date of Conference: Jan 2025
Conference Location: Waikoloa, Hawaii, United States
This presentation investigates mobile app security trends and challenges by analyzing developer discussions on Stack Overflow. Examining over 5,700 security-related questions, the study reveals how developers approach security challenges in an ecosystem with 4+ million apps and $400+ billion in revenue. The research identifies seven major security categories including secured communications, database security, and encryption, while highlighting the evolution of security discussions over time. Through a combination of quantitative analysis, topic modeling, and manual review, the study provides actionable insights for developers, educators, and researchers, helping to improve mobile app security practices and address real-world challenges faced by the development community.
Presented at: The 58th Hawaii International Conference on System Sciences (HICSS '25)
Date of Conference: Jan 2025
Conference Location: Waikoloa, Hawaii, United States
This presentation explores the use and impact of assertion messages in software testing. The study, based on a survey of 138 software practitioners, investigates the frequency, rationale, construction, and maintenance of assertion messages in test code. Key findings reveal that while assertion messages are widely used, there are challenges in maintaining their clarity and relevance. The research provides insights into how assertion messages contribute to debugging processes and offers recommendations for improving test code practices and tools.
Presented at: The 40th International Conference on Software Maintenance and Evolution (ICSME '24)
Date of Conference: Oct 2024
Conference Location: Flagstaff, AZ, United States
Preprint: https://www.peruma.me/publication/2024-icsme-assert/2024-ICSME-ASSERT.pdf
This presentation examines mobile application security practices and challenges from a developer's perspective. Based on a survey of 137 mobile app developers from 22 countries, the study investigates real-world security implementation practices, common challenges, and the effectiveness of current educational resources. Key findings highlight the importance developers place on security, commonly implemented security features, and significant gaps in mobile app security education. The research provides valuable insights into the mobile app security landscape and offers recommendations for improving security practices and developer preparation in this rapidly evolving field.
Presented at: The 40th International Conference on Software Maintenance and Evolution (ICSME '24)
Date of Conference: Oct 2024
Conference Location: Flagstaff, AZ, United States
Preprint: https://www.peruma.me/publication/2024-icsme-security/2024-ICSME-SECURITY.pdf
Presented at: The 18th International Conference on Augmented Cognition (AC ‘24)
Date of Conference: July 2024
Conference Location: Washington, DC
The preprint is available at: https://arxiv.org/abs/2404.10194
Presented at: The 6th International Conference on Technical Debt (TechDebt ‘23)
Date of Conference: May 2023
Conference Location: Melbourne, Australia
The preprint is available at: https://arxiv.org/abs/2303.02258
Presented at: The 2nd International Workshop on Natural Language-based Software Engineering (NLBSE ‘23)
Date of Conference: May 2023
Conference Location: Virtual * Melbourne, Australia
The preprint is available at: https://arxiv.org/pdf/2303.01035
This is a tutorial that was presented at: The 37th IEEE/ACM International Conference on Automated Software Engineering (ASE 2022)
Date of Conference: 10-14 October 2022
Conference Location: Oakland Center, Michigan, United States
Conference website: https://conf.researchr.org/track/ase-2022/ase-2022-tutorials#event-overview
Searching and applying for a job can be one of the most exciting and stressful times for a student. As a Ph.D. candidate, you can either join the industry or academia as a faculty member. In this talk, I will provide a walkthrough on how to navigate the academic job market. As a recent faculty hire, I will share my experience and tips from searching for openings, document and interview preparation, to finally negotiating your package.
Presented at the "What's Next? Career Talk" session organized by RIT Doctoral Student Association
Date of Presentation: 01 September 2022
Location: Virtual
In-Person presentation at: The 19th International Conference on Mining Software Repositories (MSR '22)
Date of Conference: 24 May 2022
Conference Location: Pittsburgh, PA, USA
Preprint: https://www.peruma.me/publication/2022-msr-debt/2022-MSR-DEBT.pdf
This is a tutorial that was presented at: The 20th International Conference on Software and Systems Reuse (ICSR'22)
Date of Conference: 15-17 June 2022
Conference Location: Virtual
Conference website: https://icsr2022v2.wp.imt.fr/
This is a tutorial that was presented at: The 20th International Conference on Software and Systems Reuse (ICSR'22)
Date of Conference: 15-17 June 2022
Conference Location: Virtual
Conference website: https://icsr2022v2.wp.imt.fr/
Virtual presentation at: The 19th International Conference on Mining Software Repositories (MSR '22)
Date of Conference: May 2022
Conference Location: Virtual
Preprint: https://www.peruma.me/publication/2022-msr-debt/2022-MSR-DEBT.pdf
Presented at: The 1st International Workshop on Natural Language-based Software Engineering (NLBSE ‘22)
Date of Conference: May 2022
Conference Location: Virtual
The preprint is available at: https://www.peruma.me/publication/2022-nlbse-digits/2022-nlbse-digits.pdf
A video of the presentation is available at: https://youtu.be/ERD6GTFzOxY
This document describes IDEAL, an open-source tool for detecting naming violations in code. It can detect 19 types of naming violations like methods named "get" that do more than access a property. The tool analyzes Java and C# code and provides feedback on violations. In an evaluation on over 2,000 code instances from open-source projects, it achieved an average precision of 75.27% for detecting violations. The researchers believe IDEAL could help practitioners, researchers and educators by improving code comprehension through better identifier naming.
Presented at: The 29th IEEE/ACM International Conference on Program Comprehension (ICPC '21)
Date of Conference: Tue 18 - Thu 20 May 2021
Conference Location: Virtual Conference
Presented at: The 18th International Conference on Mining Software Repositories (MSR '21)
Date of Conference: Mon 17 - Wed 19 May 2021
Conference Location: Virtual Conference
Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi
A secure test infrastructure ensures that the testing process doesn’t become a gateway for vulnerabilities. By protecting test environments, data, and access points, organizations can confidently develop and deploy software without compromising user privacy or system integrity.
What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora
Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.
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Rename Chains: An Exploratory Study on the Occurrence and Characteristics of Identifiers Undergoing Multiple Renamings
1. Rename Chains:
An Exploratory Study on the Occurrence and Characteristics of
Identifiers Undergoing Multiple Renamings
Anthony Peruma & Christian Newman
2. Overview
We explore the phenomenon of a single
identifier undergoing multiple renames (i.e., a
rename chain) through a large-scale empirical
study of 800 open-source Java systems
3. Introduction
• Research shows that developers spend 58% of their time on program
comprehension activities
• Identifier names account for 70% of the characters in the code base
• Identifier names help developers understand the purpose of the
identifier – essential that names should be high-quality
• Names must be unambiguous and intent reveling in communicating the purpose
and behavior of the code
• Developers correct poor-quality names via rename refactoring
operations – over 40% of refactoring operations are renames
• Not all renames result in a high-quality name
• An identifier can undergo multiple renaming's throughout its lifetime (i.e., a chain
of renaming's – a rename chain)
4. Rename Chain Examples
A method rename chain resulting in a more
descriptive method name in the final rename
A rename chain resulting in a weak
method name; it is just a copy of the
statement within the method
5. Related Work on Identifier Renaming
• Empirical Studies
• Arnoudova et al. – Rename taxonomy to classify the semantics updates to a name;
developer study showing renaming is not always straightforward
• Peruma et al. – Multiple studies that examine the structure and meaning of names →
developers frequently narrow the meaning of the name; grammar patterns;
contextualization; taxonomy for digits in a name
• Recommendation Models
• Allamanis et al. – utilizes statistical NLP to learn the coding style of a codebase
• Suzuki et al. – An n-gram-based approach for assessing the comprehensibility of method
names and recommending intelligible method names
• Liu et al. – Deep learning techniques to provide recommendations based on the overlap
between method bodies and names that are close in a vector space
• …
While there are studies that investigate (or involve) rename refactoring’s, this is the first
study that examines a chain of rename operations for an identifier
6. Goal & Impact
Understand the evolution of identifier
names by constructing and studying the
characteristics of a chain of renames
for identifiers (i.e., a rename chain)
Facilitate the research and development
of tools to aid in name appraisal and
recommendations
7. Research Questions
• RQ1: To what extent do identifiers undergo multiple rename
refactoring operations?
• Understand the volume and types of identifiers that undergo multiple renames
• RQ2: How frequently do renames occur within a rename chain, and
who is responsible for their creation?
• Gain insight into the developers performing the renames in the chain
• RQ3: How do the semantics of an identifier's name evolve in a
rename chain?
• Determine the lexical-semantic properties of names in the rename chain
• RQ4: To what extent can commit log messages help contextualize the
occurrence of rename chains?
• Identify the specific causes for developers to create rename chains
8. Contributions
A publicly available dataset of
rename chains for replication
or extension studies
An understanding of identifier
name evolution and a discussion
on avenues for future research
9. Experiment Design
Source dataset of rename refactorings and commit details Dataset of rename chains and their characteristics
Source Dataset: Used in prior refactoring research studies; renames mined using RefactoringMiner
Rename Chain Construction: For each project: sort renames using the author commit date; compare the
identifier’s old and new name by their fully qualified name
Part-of-Speech Tagging: Utilize a specialized identifier name part-of-speech tagger; tags for only the original and
last name in the rename chain
Topic Modeling: Commit messages associated with rename chains; preprocessing; Latent Dirichlet Allocation
RQ Analysis: Supplement our quantitative findings with qualitative examples from our dataset
11. RQ 1: To what extent do identifiers undergo multiple
rename refactoring operations?
• Identifier renaming is a common operation developers perform
• 285,786 operations: Methods: 26.50%; Parameters: 25.53%; Variables: 21.75%
• Most identifiers undergo a single rename in their lifetime
• 17,404 detected rename chains – Methods are likely to have a chain
• Methods: 30.73%; Variables: 23.47%; Classes: 16.85%
• A rename chain is usually short – composed of a median of 2 rename
instances; variables typically undergo around 3 renamings
• Rename chains tend to occur in projects frequently
• 83.71% of projects have rename chains
• Projects have a median of 9 identifiers undergoing multiple renames
12. RQ 2: How frequently do renames occur within a
rename chain, and who is responsible for their
creation?
Interval Analysis – duration between renames in the chain
• Median duration between renames is 2 days
• Attributes: 25 days; Classes: 19 days; Methods: 14 days; Variables: 7 days; Parameters:
2 days
• Duration between the first and last rename:
• Parameters have the lowest interval: 17 days
• Variables have the highest interval: 357 days
Developer Analysis – developers performing the renames
• Most chains have the same developer performing all renames: 62.05%
• Multi-developer chains have around 2 developers involved
• Attribute chains have the most number of developers: 4
• 11.51% of chains have different developers for the first and last rename
13. RQ 3: How do the semantics of an identifier's name
evolve in a rename chain?
Analysis of the lexical-semantic structure (i.e., part-of-speech tags) of
names in the rename chain
Analysis is limited to only the first and last names in the chain
• The same part-of-speech tags are used for the original and new name
• TestServlet → TheTestServlet → TestServlet : NM-N → DT-NM-N → NM-N
• Developers utilize standard naming structures:
• Class: NM-NM-N → NM-NM-N
• Attribute: NM-N → NM-N
• Method: V-NM-N → V-NM-N
• Parameter & Variable: N → N
• Usually, the original and new names are not identical – 78%
14. RQ 4: To what extent can commit log messages help
contextualize the occurrence of rename chains?
An automated analysis of rename chain commit log messages
using Latent Dirichlet Allocation
3 high-level topics associated with these messages:
• Code Cleanup – developer improving code style quality by
adhering to standards – ‘naming’, ‘convention’, ‘whitespace’
• “Lots of fixes using Checkstyle - Fixed some names to follow conventions...”
• Refactoring – developers updating the code related to the
behavior and design of the system – ‘refactor’, ‘updated’, ‘revert’
• “Major refactor to start process of eventually moving content manager classes…”
• Bug Fix/Testing – renames are part of either a bug fix or unit
testing – ‘fix’, ‘bug’, ‘test’, ‘testcase’
• “fixed bug with searching for transitive dependencies + added test for it…”
16. Overall Findings
Renaming is a common activity in software implementation
• Most identifiers typically undergo a single rename
• However, rename chains frequently occur in projects – methods are frequently associated
with rename chains
• Renames in a chain occur days apart – variables typically having the shortest duration
(approx. two days) and attributes the longest
• Renames in a rename chain are usually performed by the same developer
• Multi-developer chains usually involve two developers
• The grammatical structure of the initial and last name in the chain remains the same
• Code Cleanup, Refactoring, and Bug Fix/Testing are the cause for rename chains –
However, these topics are at a high-level due to the nature of commit messages
17. Key Takeaways
• Part-of-speech tags are an efficient means of studying the semantic
updates a name undergoes when renamed
• Academia and practitioners should not limit their focus to only the words in a name
• Improvements to name recommendations and appraisal techniques
• These techniques should consider the historical evolution of an identifier’s name in
their evaluation process
• Emphasis on the importance of using high-quality names
• Academia should instill in students the importance high-quality names in the source
code; practitioners should incorporate naming quality into code reviews
• Challenges with automated contextualization of rename chains
• Current NLP techniques are not adequate for analyzing software engineering artifacts
18. Conclusion & Future Work
• Interpreting identifier names form the backbone of any code
comprehension task
• Developers perform renames to correct poor-quality names
• This can continue throughout the system’s lifetime
• We analyze multiple renames applied to a single identifier (i.e., rename
chain)
• Almost all projects exhibit this phenomenon, with an average chain size of two
renames
• We report on characteristics such as the interval between renames, developers
responsible for chain construction, grammatical changes, and motivation
• Future work: Human subject study
• Validate our empirical findings with developers of varying experience and skills