arXiv subjects

Sridhar Chimalakonda

Publications and source records attributed to Sridhar Chimalakonda.

At least 37 records · Page 2Linked to original sources

arXiv2022

eGEN: An Energy-saving Modeling Language and Code Generator for Location-sensing of Mobile Apps

The demand for reducing the energy consumption of location-based applications has increased in recent years. The abnormal battery-draining behavior of GPS makes it difficult for the developers to decide on battery optimization during the development phase directly. It will reduce the burden on developers if battery-saving strategies are considered early, and relevant battery-aware code is generated from the design phase artifacts. Therefore, we aim to develop tool support, eGEN, to specify and create native location-based mobile apps. eGEN consists of Domain-specific Modeling Language (DSML) and a code generator for location-sensing. It is developed using Xtext and Xtend as an Eclipse plug-in, and currently, it supports native Android apps. eGEN is evaluated through controlled experiments by instrumenting the generated code in five location-based open-source Android applications. The experimental results show 4.35 minutes of average GPS reduction per hour and 188 mA of average reduction in battery consumption while showing only 97 meters degrade in location accuracy over 3 kilometers of a cycling path. Hence, we believe that code generated by eGEN would help developers to balance between energy and accuracy requirements of location-based applications. The source code, documentation, tool demo video, and tool installation video are available at https://github.com/Kowndinya2000/egen.

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arXiv2021

WAccess -- A Web Accessibility Tool based on WCAG 2.2, 2.1 and 2.0 Guidelines

The vision of providing access to all web content equally for all users makes web accessibility a fundamental goal of today's internet. Web accessibility is the practice of removing barriers from websites that could hinder functionality for users with various disabilities. Web accessibility is measured against the accessibility guidelines such as WCAG, GIGW, and so on. WCAG 2.2 is the latest set of guidelines for web accessibility that helps in making websites accessible. The web accessibility tools available in the World Wide Web Consortium (W3C), only conform up to WCAG 2.1 guidelines, while no tools exist for the latest set of guidelines. Despite the availability of several tools to check the conformity of websites with WCAG 2.1 guidelines, there is a scarcity of tools that are both open source and scalable. To support automated accessibility evaluation of numerous websites against WCAG 2.2, 2.1, and 2.0 we present a tool, WAccess. WAccess highlights violations of 13 guidelines from WCAG 2.0, 9 guidelines from WCAG 2.1, and 7 guidelines from WCAG 2.2 of a specific web page on the web console and suggests the fix for violations while specifying violating code snippet simultaneously. We evaluated WAccess against 2227 government websites of India and observed a total of about 6.1 million violations.

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arXiv2021

ML-Quest: A Game for Introducing Machine Learning Concepts to K-12 Students

Today, Machine Learning (ML) is of a great importance to society due to the availability of huge data and high computational resources. This ultimately led to the introduction of ML concepts at multiple levels of education including K-12 students to promote computational thinking. However, teaching these concepts to K-12 through traditional methodologies such as video lectures and books is challenging. Many studies in the literature have reported that using interactive environments such as games to teach computational thinking and programming improves retention capacity and motivation among students. Therefore, introducing ML concepts using a game might enhance students' understanding of the subject and motivate them to learn further. However, we are not aware of any existing game which explicitly focuses on introducing ML concepts to students using game play. Hence, in this paper, we propose ML-Quest, a 3D video game to provide conceptual overview of three ML concepts: Supervised Learning, Gradient Descent and K-Nearest Neighbor (KNN) Classification. The crux of the game is to introduce the definition and working of these concepts, which we call conceptual overview, in a simulated scenario without overwhelming students with the intricacies of ML. The game has been predominantly evaluated for its usefulness and player experience using the Technology Acceptance Model (TAM) model with the help of 23 higher-secondary school students. The survey result shows that around 70% of the participants either agree or strongly agree that the ML-Quest is quite interactive and useful in introducing them to ML concepts.

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arXiv2021

SurviveCovid-19++ : A collaborative healthcare game towards educating people about safety measures and vaccination for Covid-19

Covid-19 has been affecting population across the world for more than an year, with diverse strains of this virus being identified in many countries. Vaccines to help in curbing the virus are being developed and administered. Preventing the spread of the disease requires collaborative efforts from everyone. People with varied professional backgrounds have varied responsibilities in controlling the pandemic. It is important that everyone is aware of their respective responsibilities and also empathize with efforts and duties of other individuals. It is here, we wish to leverage the potential of games in healthcare domain, towards educating about Covid-19. With an aim to educate the population about vaccination against Covid-19, responsibilities of citizens with varied professional backgrounds, and emphasize on the need for collaboration to fight against the pandemic, by following safety measures, we present SurviveCovid-19++, a collaborative multiplayer desktop based game. The game essentially revolves around four roles - doctor, sanitation worker, citizen and law enforcer, delivering their duties, following safety measures and collaboratively clearing multiple stages in the game. We have performed a preliminary evaluation of the game through a qualitative and quantitative user survey. The results of the user survey were encouraging, with volunteers expressing their increased empathy towards efforts of individuals with varied professional backgrounds, and better understanding of the importance of safety measures against Covid-19.

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arXiv2021

COSPEX: A Program Comprehension Tool for Novice Programmers

Developers often encounter unfamiliar code during software maintenance which consumes a significant amount of time for comprehension, especially for novice programmers. Automated techniques that analyze a source code and present key information to the developers can lead to an effective comprehension of the code. Researchers have come up with automated code summarization techniques that focus on code summarization by generating brief summaries rather than aiding its comprehension. Existing debuggers represent the execution states of the program but they do not show the complete execution at a single point. Studies have revealed that the effort required for program comprehension can be reduced if novice programmers are provided with worked examples. Hence, we propose COSPEX (Comprehension using Summarization via Program Execution) - an Atom plugin that dynamically extracts key information for every line of code executed and presents it to the developers in the form of an interactive example-like dynamic information instance. As a preliminary evaluation, we presented 14 undergraduates having Python programming experience up to 1 year with a code comprehension task in a user survey. We observed that COSPEX helped novice programmers in program comprehension and improved their understanding of the code execution. The source code and tool are available at: https://bit.ly/3utHOBM, and the demo on Youtube is available at: https://bit.ly/2Sp08xQ.

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arXiv2021

SOCluster- Towards Intent-based Clustering of Stack Overflow Questions using Graph-Based Approach

Stack Overflow (SO) platform has a huge dataset of questions and answers driven by interactions between users. But the count of unanswered questions is continuously rising. This issue is common across various community Question & Answering platforms (Q&A) such as Yahoo, Quora and so on. Clustering is one of the approaches used by these communities to address this challenge. Specifically, Intent-based clustering could be leveraged to answer unanswered questions using other answered questions in the same cluster and can also improve the response time for new questions. It is here, we propose SOCluster, an approach and a tool to cluster SO questions based on intent using a graph-based clustering approach. We selected four datasets of 10k, 20k, 30k & 40k SO questions without code-snippets or images involved, and performed intent-based clustering on them. We have done a preliminary evaluation of our tool by analyzing the resultant clusters using the commonly used metrics of Silhouette coefficient, Calinkski-Harabasz Index, & Davies-Bouldin Index. We performed clustering for 8 different threshold similarity values and analyzed the intriguing trends reflected by the output clusters through the three evaluation metrics. At 90% threshold similarity, it shows the best value for the three evaluation metrics on all four datasets. The source code and tool are available for download on Github at: https://github.com/Liveitabhi/SOCluster, and the demo can be found here: https://youtu.be/uyn8ie4h3NY.

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arXiv2021

MuseumViz -- Towards Visualizing Online Museum Collections

Despite the growth of online museums for India's cultural heritage data, there is limited increase in terms of visitors. Over the years, online museums adopted many techniques to improve the overall user experience. However, many Indian online museums display artifacts as lists and grids with basic search functionality, making it less visually appealing and difficult to comprehend. Our work aims to enhance the user experience of accessing Indian online museums by utilizing advancements in information visualization. Hence, we propose MuseumViz, a framework which processes data from online museums and visualizes it using four different interactive visualizations: the Network Graph, TreepMap, Polygon Chart and SunBurst Chart. We demonstrate MuseumViz on a total of 723 cultural heritage artifacts present in the Archaeological Survey of India, Goa. Based on our evaluation with 25 users, about 83% of them find it easier and more comprehensible to browse cultural heritage artifacts through MuseumViz.

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arXiv2021

Detox Browser -- Towards Filtering Sensitive Content On the Web

The annual consumption of web-based resources is increasing at a very fast rate, mainly due to an increase in affordability and accessibility of the internet. Many are relying on the web to get diverse perspectives, but at the same time, it can expose them to content that is harmful to their mental well-being. Catchy headlines and emotionally charged articles increase the number of readers which in turn increases ad revenue for websites. When a user consumes a large quantity of negative content, it adversely impacts the user's happiness and has a significant impact on his/her mood and state of mind. Many studies carried out during the COVID-19 pandemic has shown that people across the globe irrespective of their country of origin have experienced higher levels of anxiety and depression. Web filters can help in constructing a digital environment that is more suitable for people prone to depression, anxiety and stress. A significant amount of work has been done in the field of web filtering, but there has been limited focus on helping Highly Sensitive Persons (HSP's) or those with stress disorders induced by trauma. Through this paper, we propose detox Browser, a simple tool that enables end-users to tune out of or control their exposure to topics that can affect their mental well being. The extension makes use of sentiment analysis and keywords to filter out flagged content from google search results and warns users if any blacklisted topics are detected when navigating across websites

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arXiv2021

APIScanner -- Towards Automated Detection of Deprecated APIs in Python Libraries

Python libraries are widely used for machine learning and scientific computing tasks today. APIs in Python libraries are deprecated due to feature enhancements and bug fixes in the same way as in other languages. These deprecated APIs are discouraged from being used in further software development. Manually detecting and replacing deprecated APIs is a tedious and time-consuming task due to the large number of API calls used in the projects. Moreover, the lack of proper documentation for these deprecated APIs makes the task challenging. To address this challenge, we propose an algorithm and a tool APIScanner that automatically detects deprecated APIs in Python libraries. This algorithm parses the source code of the libraries using abstract syntax tree (ASTs) and identifies the deprecated APIs via decorator, hard-coded warning or comments. APIScanner is a Visual Studio Code Extension that highlights and warns the developer on the use of deprecated API elements while writing the source code. The tool can help developers to avoid using deprecated API elements without the execution of code. We tested our algorithm and tool on six popular Python libraries, which detected 838 of 871 deprecated API elements. Demo of APIScanner: https://youtu.be/1hy_ugf-iek. Documentation, tool, and source code can be found here: https://rishitha957.github.io/APIScanner.

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arXiv2021

A Catalogue of Game-Specific Software Nuggets

With the ever-increasing use of games, game developers are expected to write efficient code supporting several qualities such as security, maintainability, and performance. However, the continuous need to update the features of games in less duration might compel the developers to use anti-patterns, code smells and quick-fix solutions that may affect the functional and non-functional requirements of the game. These bad practices may lead to technical debt, poor program comprehension, and can cause several issues during software maintenance. Here, in this paper, we introduce "Software Nuggets" as a concept that affects software quality in a negative way and as a superset of anti-patterns, code smells, bugs, software bad practices. We call these Software Nuggets as "G-Nuggets" in the context of games. While there exists empirical research on games, we are not aware of any work on understanding and cataloguing these G-Nuggets. Thus, we propose a catalogue of G-Nuggets by mining and analyzing 892 commits, 189 issues, and 104 pull requests from 100 open-source GitHub game repositories. We use regular expressions and thematic analysis on this dataset for cataloguing game-specific Software Nuggets. We present a catalogue of ten G-Nuggets and provide examples for them present online at: https://phoebs88.github.io/A-Catalogue-of-Game-Specific-Software-Nuggets. We believe this catalogue might be helpful for researchers for further empirical research in the domain of games as well as for game developers to improve quality of games.

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arXiv2021

SurviveCovid-19 -- An Educational Game to Facilitate Habituation of Social Distancing and Other Health Measures for Covid-19 Pandemic

Covid-19 has been causing severe loss to the human race. Considering the mode of spread and severity, it is essential to make it a habit to follow various safety precautions such as using sanitizers and masks and maintaining social distancing to prevent the spread of Covid-19. Individuals are widely educated about the safety measures against the disease through various modes such as announcements through online or physical awareness campaigns, advertisements in the media and so on. The younger generations today spend considerably more time on mobile phones and games. However, there are very few applications or games aimed to help in practicing safety measures against a pandemic, which is much lesser in the case of Covid-19. Hence, we propose a 2D survival-based game, SurviveCovid-19, aimed to educate people about safety precautions to be taken for Covid-19 outside their homes by incorporating social distancing and usage of masks and sanitizers in the game. SurviveCovid-19 has been designed as an Android-based mobile game, along with a desktop (browser) version, and has been evaluated through a remote quantitative user survey, with 30 volunteers using the questionnaire based on the MEEGA+ model. The survey results are promising, with all the survey questions having a mean value greater than 3.5. The game's quality factor was 69.3, indicating that the game could be classified as excellent quality, according to the MEEGA+ model.

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arXiv2021

Apples, Oranges & Fruits -- Understanding Similarity of Software Projects Through The Lens of Dissimilar Artifacts

The growing availability of open source projects has facilitated developers to reuse existing software artifacts and leverage them to develop new software. However, it is hard to understand the notion of similarity as it varies from developer to developer. Some developers might search for repositories with similar source code, while some might be in search of repositories with similar requirements or issues. Existing approaches tend to find similar projects by comparing similar artifacts such as source-code to source-code, API usage to API usage, documentation to documentation, and so on. Even though there is a dissimilarity between two similar artifacts, there could be a similarity between two dissimilar artifacts. Hence, in this paper, we aim to answer the question - Can we find similarity of software repositories through dissimilar artifacts?. To this end, we conduct an experiment to find similarities between three repositories, two similar and one different project comparing similar and dissimilar artifacts (documentation, commits, and source-code). We observed similarities between dissimilar artifacts such as Commits, Source Code, and Readme Files in the context of both similar and different repositories.

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arXiv2021

What's in a GitHub Repository? -- A Software Documentation Perspective

Developers use and contribute to repositories on GitHub. Documentation present in the repositories serves as an important source by helping developers to understand, maintain and contribute to the project. Currently, documentation in a repository is diversified, among various files, with most of it present in ReadMe files. However, other software artifacts in the repository, such as issue reports and pull requests could also contribute to documentation, without documentation being explicitly specified. Hence, in this paper, we propose a taxonomy of documentation sources by analyzing different software artifacts, developer interviews and card-sorting approach. We inspected multiple artifacts of 950 public GitHub repositories, written in four different programming languages, C++, C#, Python and Java, and analyzed the type and amount of documentation that could be extracted from these artifacts. To this end, we observe that, about 25.93% of information extracted from all sources proposed in the taxonomy contains error-related documentation, and that pull requests contribute to around 18.21% of extracted information.

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arXiv2021

Understanding Emotions of Developer Community Towards Software Documentation

The availability of open-source projects facilitates developers to contribute and collaborate on a wide range of projects. As a result, the developer community contributing to such open-source projects is also increasing. Many of the projects involve frequent updates and extensive reuses. A well-updated documentation helps in a better understanding of the software project and also facilitates efficient contribution and reuse. Though software documentation plays an important role in the development and maintenance of software, it also suffers from various issues that include insufficiency, inconsistency, ill-maintainability, and so on. Exploring the perception of developers towards documentation could help in understanding the reasons behind prevalent issues in software documentation. It could further aid in deciding on training that could be given to the developer community towards building more sustainable projects for society. Analyzing sentiments of contributors to a project could provide insights on understanding developer perceptions. Hence, as the first step towards this direction, we analyze sentiments of commit messages specific to the documentation of a software project. To this end, we considered the commit history of 998 GitHub projects from the GHTorrent dataset and identified 10,996 commits that correspond to the documentation of repositories. Further, we apply sentiment analysis techniques to obtain insights on the type of sentiment being expressed in commit messages of the selected commits. We observe that around 45% of the identified commit messages express trust emotion.

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arXiv2020

AC2 -- Towards Understanding Architectural Changes in Rapid Releases

Open source projects are adopting faster release cycles that reflect various changes. Therefore, comprehending the effects of these changes on software's architecture over the releases becomes necessary. However, it is challenging to keep architecture in-check and add new changes simultaneously for every release. To this end, we propose a visualization tool called AC2, which allows its users to examine the alterations in the architecture at both higher and lower levels of abstraction for the python projects. AC2 uses call graphs and collaboration graphs to show the interaction between different architectural components. The tool provides four different views to see the architectural changes. The user can examine two releases at a time to comprehend the architectural changes between the releases. AC2 can support the maintainers and developers to observe changes in the project and its influence on the architecture, which allow them to see its increasing complexity over the releases at the component level. AC2 can be downloaded at https://github.com/dheerajrox/AC2 and its demo can be seen at the website https://dheerajrox.github.io/AC2doc or on youtube https://www.youtube.com/watch?v=GNrJfZ0RCVI

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arXiv2020

DRAST -- A Deep Learning and AST Based Approach for Bug Localization

Context: Given a bug report and source code of the project, bug localization can help developers to focus on fixing probable buggy files rather than searching the entire source code repository. While existing research uses information retrieval (IR) and/or combination of machine learning (ML) or deep learning (DL) approaches, they focus primarily on benchmark Java projects, and also motivate the need for multi-language bug localization approach. Objective: To create a novel bug localization approach that leverages the syntactic structure of source code, bug report information and which can support multi-language projects along with a new dataset of C projects. Method: The proposed DRAST approach represents source code as code vectors by using its high-level AST and combines rVSM, an IR technique with ML/DL models such as Random Forest and Deep Neural Network regressor to rank the list of buggy files. We also use features such as textual similarity using IR techniques, lexical mismatch using DNNs, and history of the project using the metadata of BugC dataset. Results: We tested DRAST on seven projects from the BugC dataset, which consists of 2462 bug reports from 21 open-source C projects. The results show that DRAST can locate correct buggy files 90% of the time from top 1, 5, and 10 suggested files with MAP and MRR scores of above 90% for the randomly selected seven projects. We also tested DRAST on Tomcat and AspectJ, projects from benchmark dataset with better results at accuracy@1, MAP and MRR when compared with state-of-the-art. Conclusions: This paper presents a novel bug localization approach that works on C and Java projects and a bug localization C dataset along with a novel source code representation. The results for C projects using DRAST are promising and could motivate researchers/practitioners to focus on developing and creating multi-language bug localization approaches.

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arXiv2020

EmoG- Towards Emojifying Gmail Conversations

Emails are one of the most frequently used medium of communication in the present day across multiple domains including industry and educational institutions. Understanding sentiments being expressed in an email could have a considerable impact on the recipients' action or response to the email. However, it is difficult to interpret emotions of the sender from pure text in which emotions are not explicitly present. Researchers have tried to predict customer attrition by integrating emails in client-company environment with emotions. However, most of the existing works deal with static assessment of email emotions. Presenting sentiments of emails dynamically to the reader could help in understanding senders' emotion and as well have an impact on readers' action. Hence, in this paper, we present EmoG as a Google Chrome Extension which is intended to support university students. It augments emails with emojis based on the sentiment being conveyed in the email, which might also offer faster overview of email sentiments and act as tags that could help in automatic sorting and processing of emails. Currently, EmoG has been developed to support Gmail inbox on a Google Chrome browser, and could be extended to other inboxes and browsers with ease. We have conducted a user survey with 15 university students to understand the usefulness of EmoG and received positive feedback.

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arXiv2020

StackEmo-Towards Enhancing User Experience by Augmenting Stack Overflow with Emojis

With the increase in acceptance of open source platforms for knowledge sharing, Question and Answer (Q\&A) websites such as Stack Overflow have become increasingly popular in the programming domain. Many novice programmers visit Stack Overflow for reasons that include posing questions, finding answers for issues they come across in the process of programming. Practitioners voluntarily answer questions on Stack Overflow based on their experience or prior knowledge. Most of these answers are also accompanied by comments from users of Stack Overflow. Questions, answers and comments on Stack Overflow also include sentiments of users, which when analysed and presented could motivate users in reading and contributing to the posts. However, the sentiment of these posts is not being depicted in the current Stack Overflow platform. There is extensive research on analysing sentiments on social networking platforms such as twitter. Representing sentiment of a post might motivate users to follow or answer certain posts. While there exist several tools that augment or annotate Stack Overflow platform for developers, we are not aware of tools that deal with sentiment of the posts. In this paper, we propose StackEmo as a Google Chrome plugin to augment comments on Stack Overflow with emojis, based on the sentiment of the comments posted, with the aim to provide users with visual cues that could motivate the users to review and contribute to available comments. We evaluated StackEmo through an in-user likert scale based survey with 30 university students. The results of the survey provided us insights on improving StackEmo, with 83% participants having recommended the plugin to their peers.

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