Network Topology and it's types Star topology, Ring topology, Bus topology an...Self-employed
this slides will help you in understanding what is network topology and its types Bus topology, Ring topology, Star topology and Mesh topology and also what is a Hub, Router and Switch.
Network Topology and it's types Star topology, Ring topology, Bus topology an...Self-employed
this slides will help you in understanding what is network topology and its types Bus topology, Ring topology, Star topology and Mesh topology and also what is a Hub, Router and Switch.
The data link layer, or layer 2, is the second layer of the seven-layer OSI model of computer networking. This layer is the protocol layer that transfers data between adjacent network nodes in a wide area network (WAN) or between nodes on the same local area network (LAN) segment.
A COMPARATIVE ANALYSIS ON SOFTWARE ARCHITECTURE STYLESADEIJ Journal
Software architecture is the structural solution that achieves the overall technical and operational
requirements for software developments. Software engineers applied software architectures for their
software system developments; however, they worry the basic benchmarks in order to select software
architecture styles, possible components, integration methods (connectors) and the exact application of
each style.
The objective of this research work was a comparative analysis of software architecture styles by its
weakness and benefits in order to select by the programmer during their design time. Finally, in this study,
the researcher has been identified architectural styles, weakness, and Strength and application areas with
its component, connector and Interface for the selected architectural styles.
Haystack 2018 - Algorithmic Extraction of Keywords Concepts and VocabulariesMax Irwin
Presentation as given to the Haystack Conference, which outlines research and techniques for automatic extraction of keywords, concepts, and vocabularies from text corpora.
The data link layer, or layer 2, is the second layer of the seven-layer OSI model of computer networking. This layer is the protocol layer that transfers data between adjacent network nodes in a wide area network (WAN) or between nodes on the same local area network (LAN) segment.
A COMPARATIVE ANALYSIS ON SOFTWARE ARCHITECTURE STYLESADEIJ Journal
Software architecture is the structural solution that achieves the overall technical and operational
requirements for software developments. Software engineers applied software architectures for their
software system developments; however, they worry the basic benchmarks in order to select software
architecture styles, possible components, integration methods (connectors) and the exact application of
each style.
The objective of this research work was a comparative analysis of software architecture styles by its
weakness and benefits in order to select by the programmer during their design time. Finally, in this study,
the researcher has been identified architectural styles, weakness, and Strength and application areas with
its component, connector and Interface for the selected architectural styles.
Haystack 2018 - Algorithmic Extraction of Keywords Concepts and VocabulariesMax Irwin
Presentation as given to the Haystack Conference, which outlines research and techniques for automatic extraction of keywords, concepts, and vocabularies from text corpora.
Named Entity Recognition For Hindi-English code-mixed Twitter Text Amogh Kawle
Speakers often switch back and forth between languages when speaking or writing, mostly in informal settings. This language interchanging involves complex grammar and the terms “code switching” or “code mixing” are used to describe It .
Webinar: Simpler Semantic Search with SolrLucidworks
Hear from Lucidworks Senior Solutions Consultant Ted Sullivan about how you can leverage Apache Solr and Lucidworks Fusion to improve semantic awareness of your search applications.
These slides contain an introduction to Symbolic execution and an introduction to KLEE.
I made this for a small demo/intro for my research group's meeting.
Grammarly AI-NLP Club #6 - Sequence Tagging using Neural Networks - Artem Che...Grammarly
Speaker: Artem Chernodub, Chief Scientist at Clikque Technology and Associate Professor at Ukrainian Catholic University
Summary: Sequence Tagging is an important NLP problem that has several applications, including Named Entity Recognition, Part-of-Speech Tagging, and Argument Component Detection. In our talk, we will focus on a BiLSTM+CNN+CRF model — one of the most popular and efficient neural network-based models for tagging. We will discuss task decomposition for this model, explore the internal design of its components, and provide the ablation study for them on the well-known NER 2003 shared task dataset.
May 2012 JaxDUG presentation by Zachary Gramana on using the Lucene.NET library to add search functionality to .NET applications. Contains an overview of search/information retrieval concepts and highlights some common use-cases.
Feel free to share to every aspiring ICT SHS teacher that is starting out. Just please do not take the copyright credit. The content is taken from Rex and Abiva Empowerment Technologies books.
Illustrated Code: Building Software in a Literate Way
Andreas Zeller, CISPA Helmholtz Center for Information Security
Notebooks – rich, interactive documents that join together code, documentation, and outputs – are all the rage with data scientists. But can they be used for actual software development? In this talk, I share experiences from authoring two interactive textbooks – fuzzingbook.org and debuggingbook.org – and show how notebooks not only serve for exploring and explaining code and data, but also how they can be used as software modules, integrating self-checking documentation, tests, and tutorials all in one place. The resulting software focuses on the essential, is well-documented, highly maintainable, easily extensible, and has a much higher shelf life than the "duct tape and wire” prototypes frequently found in research and beyond.
Just Click on Below Link To Download This Course:
https://www.devrycoursehelp.com/product/devry-cis-355a-full-course-latest/
Go to a job posting site (CareerBuilder, Dice, ComputerJobs, etc.) or use search engines to find Java developer or Java programmer positions. Copy and paste the job posting into the Discussion area. Briefly explore all the topics that you will learn in this class this session. What are the skills you will learn in this course that are also requirements for the positions you see posted by you and your classmates?
Finding Some "Good" iOS Interview Questions for Employersnataliepo
Why is it so damn hard to find a good developer? How can I tell if someone knows enough to work with me without me losing my mind? We have little to no standards on how to quantify how qualified an iOS Developer is, so it makes it that much harder to really know what you're getting when you interview a potential candidate.
In this talk (given at CocoaHeads NYC in May 2015), we explore a set of interview questions, and, as a varied-level group, we took the challenge ourselves, and attempted to rate how difficult the questions would be for junior, senior, and expert level iOS Developers.
LeakPair: Proactive Repairing of Leaks in Single Page Web ApplicationsDongsun Kim
Modern web applications often resort to application development frameworks such as React, Vue.js, and Angular. While the frameworks facilitate the development of web applications with several useful components, they are inevitably vulnerable to unmanaged memory consumption since the frameworks often produce Single Page Applications (SPAs). Web applications can be alive for hours and days with behavior loops, in such cases, even a single memory leak in a SPA app can cause performance degradation on the client side. However, recent debugging techniques for web applications still focus on memory leak detection, which requires manual tasks and produces imprecise results.
We propose LeakPair, a technique to repair memory leaks in single page applications. Given the insight that memory leaks are mostly non-functional bugs and fixing them might not change the behavior of an application, the technique is designed to proactively generate patches to fix memory leaks, without leak detection, which is often heavy and tedious. To generate effective patches, LeakPair follows the idea of pattern-based program repair since the automated repair strategy shows successful results in many recent studies. We evaluate the technique on more than 20 open-source projects without using explicit leak detection. The patches generated by our technique are also submitted to the projects as pull requests. The results show that LeakPair can generate effective patches to reduce memory consumption that are acceptable to developers. In addition, we execute the test suites given by the projects after applying the patches, and it turns out that the patches do not cause any functionality breakage; this might imply that LeakPair can generate non-intrusive patches for memory leaks.
iFixR: Bug Report Driven Program RepairDongsun Kim
Issue tracking systems are commonly used in modern software development for collecting feedback from users and developers. An ultimate automation target of software maintenance is then the systematization of patch generation for user-reported bugs. Although this ambition is aligned with the momentum of automated program repair, the literature has, so far, mostly focused on generate-and- validate setups where fault localization and patch generation are driven by a well-defined test suite. On the one hand, however, the common (yet strong) assumption on the existence of relevant test cases does not hold in practice for most development settings: many bugs are reported without the available test suite being able to reveal them. On the other hand, for many projects, the number of bug reports generally outstrips the resources available to triage them. Towards increasing the adoption of patch generation tools by practitioners, we investigate a new repair pipeline, iFixR, driven by bug reports: (1) bug reports are fed to an IR-based fault localizer; (2) patches are generated from fix patterns and validated via regression testing; (3) a prioritized list of generated patches is proposed to developers. We evaluate iFixR on the Defects4J dataset, which we enriched (i.e., faults are linked to bug reports) and carefully-reorganized (i.e., the timeline of test-cases is naturally split). iFixR generates genuine/plausible patches for 21/44 Defects4J faults with its IR-based fault localizer. iFixR accurately places a genuine/plausible patch among its top-5 recommendation for 8/13 of these faults (without using future test cases in generation-and-validation).
TBar: Revisiting Template-based Automated Program RepairDongsun Kim
We revisit the performance of template-based APR to build comprehensive knowledge about the effectiveness of fix patterns, and to highlight the importance of complementary steps such as fault localization or donor code retrieval. To that end, we first investigate the literature to collect, summarize and label recurrently-used fix patterns. Based on the investigation, we build TBar, a straightforward APR tool that systematically attempts to apply these fix patterns to program bugs. We thoroughly evaluate TBar on the Defects4J benchmark. In particular, we assess the actual qualitative and quantitative diversity of fix patterns, as well as their effectiveness in yielding plausible or correct patches. Eventually, we find that, assuming a perfect fault localization, TBar correctly/plausibly fixes 74/101 bugs. Replicating a standard and practical pipeline of APR assessment, we demonstrate that TBar correctly fixes 43 bugs from Defects4J, an unprecedented performance in the literature (including all approaches, i.e., template-based, stochastic mutation-based or synthesis-based APR).
Mining Fix Patterns for FindBugs ViolationsDongsun Kim
Several static analysis tools, such as Splint or FindBugs, have been proposed to the software development community to help detect security vulnerabilities or bad programming practices. However, the adoption of these tools is hindered by their high false positive rates. If the false positive rate is too high, developers may get acclimated to violation reports from these tools, causing concrete and severe bugs being overlooked. Fortunately, some violations are actually addressed and resolved by developers. We claim that those violations that are recurrently fixed are likely to be true positives, and an automated approach can learn to repair similar unseen violations. However, there is lack of a systematic way to investigate the distributions on existing violations and fixed ones in the wild, that can provide insights into prioritizing violations for developers, and an effective way to mine code and fix patterns which can help developers easily understand the reasons of leading violations and how to fix them.
In this paper, we first collect and track a large number of fixed and unfixed violations across revisions of software. The empirical analyses reveal that there are discrepancies in the distributions of violations that are detected and those that are fixed, in terms of occurrences, spread and categories, which can provide insights into prioritizing violations. To automatically identify patterns in violations and their fixes, we propose an approach that utilizes convolutional neural networks to learn features and clustering to regroup similar instances. We then evaluate the usefulness of the identified fix patterns by applying them to unfixed violations. The results show that developers will accept and merge a majority (69/116) of fixes generated from the inferred fix patterns. It is also noteworthy that the yielded patterns are applicable to four real bugs in the Defects4J major benchmark for software testing and automated repair.
Learning to Spot and Refactor Inconsistent Method NamesDongsun Kim
To ensure code readability and facilitate software maintenance, program methods must be named properly. In particular, method names must be consistent with the corresponding method implementations. Debugging method names remains an important topic in the literature, where various approaches analyze commonalities among method names in a large dataset to detect inconsistent method names and suggest better ones. We note that the state-of-the-art does not analyze the implemented code itself to assess consistency. We thus propose a novel automated approach to debugging method names based on the analysis of consistency between method names and method code. The approach leverages deep feature representation techniques adapted to the nature of each artifact. Experimental results on over 2.1 million Java methods show that we can achieve up to 15 percentage points improvement over the state-of-the-art, establishing a record performance of 67.9% F1-measure in identifying inconsistent method names. We further demonstrate that our approach yields up to 25% accuracy in suggesting full names, while the state-of-the-art lags far behind at 1.1% accuracy. Finally, we report on our success in fixing 66 inconsistent method names in a live study on projects in the wild.
You Cannot Fix What You Cannot Find! --- An Investigation of Fault Localizati...Dongsun Kim
Properly benchmarking Automated Program Re- pair (APR) systems should contribute to the development and adoption of the research outputs by practitioners. To that end, the research community must ensure that it reaches significant milestones by reliably comparing state-of-the-art tools for a better understanding of their strengths and weaknesses. In this work, we identify and investigate a practical bias caused by the fault localization (FL) step in a repair pipeline. We propose to highlight the different fault localization configurations used in the literature, and their impact on APR systems when applied to the Defects4J benchmark. Then, we explore the performance variations that can be achieved by “tweaking” the FL step. Eventually, we expect to create a new momentum for (1) full disclosure of APR experimental procedures with respect to FL, (2) realistic expectations of repairing bugs in Defects4J, as well as (3) reliable performance comparison among the state-of-the- art APR systems, and against the baseline performance results of our thoroughly assessed kPAR repair tool. Our main findings include: (a) only a subset of Defects4J bugs can be currently localized by commonly-used FL techniques; (b) current practice of comparing state-of-the-art APR systems (i.e., counting the number of fixed bugs) is potentially misleading due to the bias of FL configurations; and (c) APR authors do not properly qualify their performance achievement with respect to the different tuning parameters implemented in APR systems.
Bug fixing is a time-consuming and tedious task. To reduce the manual efforts in bug fixing, researchers have pre- sented automated approaches to software repair. Unfortunately, recent studies have shown that the state-of-the-art techniques in automated repair tend to generate patches only for a small number of bugs even with quality issues (e.g., incorrect behavior and nonsensical changes). To improve automated program repair (APR) techniques, the community should deepen its knowledge on repair actions from real-world patches since most of the techniques rely on patches written by human developers. Previous investigations on real-world patches are limited to statement level that is not sufficiently fine-grained to build this knowledge. In this work, we contribute to building this knowledge via a systematic and fine-grained study of 16,450 bug fix commits from seven Java open-source projects. We find that there are opportunities for APR techniques to improve their effectiveness by looking at code elements that have not yet been investigated. We also discuss nine insights into tuning automated repair tools. For example, a small number of statement and expression types are recurrently impacted by real-world patches, and expression-level granularity could reduce search space of finding fix ingredients, where previous studies never explored.
LSRepair: Live Search of Fix Ingredients for Automated Program RepairDongsun Kim
Automated program repair (APR) has extensively been developed by leveraging search-based techniques, in which fix ingredients are explored and identified in different granular- ities from a specific search space. State-of-the approaches often find fix ingredients by using mutation operators or leveraging manually-crafted templates. We argue that the fix ingredients can be searched in an online mode, leveraging code search techniques to find potentially-fixed versions of buggy code fragments from which repair actions can be extracted. In this study, we present an APR tool, LSRepair, that automatically explores code repositories to search for fix ingredients at the method-level granularity with three strategies of similar code search. Our preliminary evaluation shows that code search can drive a faster fix process (some bugs are fixed in a few seconds). LSRepair helps repair 19 bugs from the Defects4J benchmark successfully. We expect our approach to open new directions for fixing multiple-lines bugs.
AVATAR : Fixing Semantic Bugs with Fix Patterns of Static Analysis ViolationsDongsun Kim
Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-the- art pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.
Bench4BL: Reproducibility Study on the Performance of IR-Based Bug LocalizationDongsun Kim
Jaekwon Lee, Dongsun Kim, Tegawendé F. Bissyandé, Woosung Jung and Yves Le Traon, “Bench4BL: Reproducibility Study on the Performance of IR-Based Bug Localization”, in Proceedings of the 27th International Symposium on Software Testing and Analysis (ISSTA 2018), Amsterdam, Netherlands, July 16 – 21, 2018.
Impact of Tool Support in Patch ConstructionDongsun Kim
Anil Koyuncu, Tegawendé F. Bissyandé, Dongsun Kim, Jacques Klein, Martin Monperrus, and Yves Le Traon, “Impact of Tool Support in Patch Construction,” in Proceedings of the 26th International Symposium on Software Testing and Analysis (ISSTA 2017), Santa Barbara, California, United States, July 10-14, 2017.
Augmenting and structuring user queries to support efficient free-form code s...Dongsun Kim
Presented at the 40th International Conference on Software Engineering, May 27 - 3 June 2018, Gothenburg, Sweden (ICSE 2018) as Journal first publication.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
AI Genie Review: World’s First Open AI WordPress Website CreatorGoogle
AI Genie Review: World’s First Open AI WordPress Website Creator
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✅Publish Automated Posts and Pages using AI Genie directly on Your website
✅50 DFY Websites Included Without Adding Any Images, Content Or Doing Anything Yourself
✅Integrated Chat GPT Bot gives Instant Answers on Your Website to Visitors
✅Just Enter the title, and your Content for Pages and Posts will be ready on your website
✅Automatically insert visually appealing images into posts based on keywords and titles.
✅Choose the temperature of the content and control its randomness.
✅Control the length of the content to be generated.
✅Never Worry About Paying Huge Money Monthly To Top Content Creation Platforms
✅100% Easy-to-Use, Newbie-Friendly Technology
✅30-Days Money-Back Guarantee
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIGenieApp #AIGenieBonus #AIGenieBonuses #AIGenieDemo #AIGenieDownload #AIGenieLegit #AIGenieLiveDemo #AIGenieOTO #AIGeniePreview #AIGenieReview #AIGenieReviewandBonus #AIGenieScamorLegit #AIGenieSoftware #AIGenieUpgrades #AIGenieUpsells #HowDoesAlGenie #HowtoBuyAIGenie #HowtoMakeMoneywithAIGenie #MakeMoneyOnline #MakeMoneywithAIGenie
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
GOING AOT WITH GRAALVM FOR SPRING BOOT (SPRING IO)
FaCoY – A Code-to-Code Search Engine
1. FaCoY – A code-to-code
search engine
Kisub Kim1, Dongsun Kim1,Tegawendé F. Bissyandé1,
Eunjong Choi2, Li Li3, Jacques Klein1, andYves Le Traon1
1SnT, University of Luxembourg - Luxembourg
2Nara Institute of Science and Technology (NAIST) - Japan
3Faculty of Information Technology, Monash University - Australia
1
01. 06. 2018
SerVal
3.1 - the Interdisciplinary Centre for
Security Reliability and Trust 1.1 - logotype of the University
of Luxembourg
2. function boolean hasLoop(Node startNode){
Node currentNode = startNode;
while (currentNode = currentNode.next());
return false;
}
I want to find a loop in a singly linked list
2
3. I want to find a loop in a singly linked list
function boolean hasLoop(Node startNode){
Node currentNode = startNode;
while (currentNode = currentNode.next());
return false;
}
Is this correct?
Any better implementation?
How can I improve my code?
3
6. State-of-the-art
Static Approaches
Dynamic Approaches
- Code clone detection techniques.
- Mostly focus on textually, structurally or syntactically similar code.
- Leverage various intermediate representations to compute code similarity:
Static approaches tend to miss fragments that have similar behaviour.
- Identify programs that retrieve similar results for the same inputs.
- Generate random inputs, rely on symbolic, concolic execution,
check abstract memory states.
- Compare instruction-level execution traces [DyCLINK @FSE16]
Dynamic approaches do not scale to large repositories and are not
always operational.
6
• Token-based [CCFinder @TSE02]
• AST-based [Deckard @ICSE07]
• Graph-based [GPLAG @KDD06]
9. Conceptual Steps
9
Code fragment
What functionality
is implemented?
What are related
implementations?
What are other representative
tokens for the functionality?
Q&A Posts (questions + code snippets)
Similar Code
fragments
Which code fragments best match
these tokens?
Target code base
10. Conceptual Steps
9
Code fragment
What functionality
is implemented?
What are related
implementations?
What are other representative
tokens for the functionality?
Q&A Posts (questions + code snippets)
Similar Code
fragments
Which code fragments best match
these tokens?
Target code base
11. Conceptual Steps
9
Code fragment
What functionality
is implemented?
What are related
implementations?
What are other representative
tokens for the functionality?
Q&A Posts (questions + code snippets)
Similar Code
fragments
Which code fragments best match
these tokens?
Target code base
12. Conceptual Steps
9
Code fragment
What functionality
is implemented?
What are related
implementations?
What are other representative
tokens for the functionality?
Q&A Posts (questions + code snippets)
Similar Code
fragments
Which code fragments best match
these tokens?
Target code base
13. Conceptual Steps
9
Code fragment
What functionality
is implemented?
What are related
implementations?
What are other representative
tokens for the functionality?
Q&A Posts (questions + code snippets)
Similar Code
fragments
Which code fragments best match
these tokens?
Target code base
15. How it works (Visually)
Input code
11
function boolean hasLoop(Node startNode){
Node currentNode = startNode;
while (currentNode = currentNode.next());
return false;
}
16. How it works (Visually)
Input code
11
function boolean hasLoop(Node startNode){
Node currentNode = startNode;
while (currentNode = currentNode.next());
return false;
}
used_classes : Node
used_classes : hasLoop
methods_called : next
class_instance_creation : currentNode
17. How it works (Visually)
Input code
Syntactically
similar code
11
function boolean hasLoop(Node startNode){
Node currentNode = startNode;
while (currentNode = currentNode.next());
return false;
}
used_classes : Node
used_classes : hasLoop
methods_called : next
class_instance_creation : currentNode
18. How it works (Visually)
Input code
Syntactically
similar code
Natural language
description
of input code
11
function boolean hasLoop(Node startNode){
Node currentNode = startNode;
while (currentNode = currentNode.next());
return false;
}
used_classes : Node
used_classes : hasLoop
methods_called : next
class_instance_creation : currentNode
19. How it works (Visually)
Input code
Syntactically
similar code
Natural language
description
of input code
11
function boolean hasLoop(Node startNode){
Node currentNode = startNode;
while (currentNode = currentNode.next());
return false;
}
used_classes : Node
used_classes : hasLoop
methods_called : next
class_instance_creation : currentNode
27. Step 0: Dataset Indexing
• Parse the code snippets from answer posts and
code files to generate an Abstract Syntax Tree (AST).
• Preprocess the natural language text from posts.
• Indexed as inverted indices.
Answer
Posts
Post Analyzer
Snippet
Index
Code Snippet
with Metadata
Question
Posts
Post Analyzer
Question
Index
Full Post
Information
Project
Repository
Codes
Code Analyzer
Project
Code Index
14
29. User Input
Answer Snippets
Code
Query
Code Fragment
(2)
Generating
Code Query
(1)
Question
Answer
Snippet
Searching for
Similar Code
Snippets
Stack
Overflow
Step 2: Syntactic Search in Q&A Answers
16
30. User Input
Answer Snippets
Code
Query
Code Fragment
(2)
(3)
Question Posts
Generating
Code Query
(1)
Question
Answer
Snippet
Question
Answer
Snippet
Searching for
Similar Code
Snippets
Searching for
Similar
Questions
Stack
Overflow
Step 3: Collection of Descriptive Natural
Language terms
17
31. User Input
Answer Snippets
Code
Query
Code Fragment
(2)
(3)
Question Posts
Generating
Code Query
(1)
Question
Answer
Snippet
Question
Answer
Snippet
Searching for
Similar Code
Snippets
Searching for
Similar
Questions
Stack
Overflow
Generating
Alternate
Code Query
Code
Queries
(4)
Step 4: Query Alternation
18
32. User Input
Answer Snippets
Code
Query
Code Fragment
(2)
(3)
Question Posts
Generating
Code Query
(1)
Question
Answer
Snippet
Question
Answer
Snippet
Searching for
Similar Code
Snippets
Searching for
Similar
Questions
Stack
Overflow
Generating
Alternate
Code Query
Code
Queries
(4)
GitHub Codebase
Search Results
(5)
Searching for
Code
Examples
Step 5: Results Retrieving from Codebase
19
33. User Input
Snippet
Index
Code
Index
Code
Query
Generating
Code Query
Code
Query
Code Fragment
Search Results
(2)
(3)
(4)(5)
Question
Index
Generating
Code Query
(1)
Question
Answer
Snippet
Question
Answer
Snippet
Searching for
Similar Code
Snippets
Searching for
Similar
Questions
Searching for
Code
Examples
Overview of the Approach
20
38. RQ1
How relevant are code examples found by FaCoY compared to other code-
to-code search engines?
RQ2
What is the effectiveness of FaCoY including semantic clones based on a
code clone benchmark?
RQ3
Do the semantically similar code fragments yielded by FaCoY exhibit
similar runtime behavior?
RQ4 Could FaCoY recommend correct code as alternative of buggy code?
Research Questions
24
39. Code Clone Type Definitions
Clone Type Definition
Type-1 Identical code fragments, except for white-space, layout, and comments
Type-2
Identical code fragments, except for identifier names and literal values
+ Type-1
Type-3
Syntactically similar, but statements are added, modified and/or
removed with respect to each other + Type-1 and Type-2
Type-4
Syntactically dissimilar, but the same functionality
= Semantic clones.
25
• Stefan Bellon, Rainer Koschke, Guildo Antoniol, Jens Krinke, and Ettore Merlo, 2007. Comparison and evaluation of clone detection tools. IEEE
Transactions on Software Engineering 22, 9 (2007), 557-591.
• Chanchal K Roy, James R Cordy, and Rainer Koschke, 2009. Comparison and evaluation of code clone detection techniques and tools: A qualitative
approach. Science of Computer Programming 74, 7 (2009), 470-395.
• Fang-Hsiang Su, Jonathan Bell, Kenneth Harvey, Simha Sethumadhavan, Gail Kaiser, and Tony Jebara, 2016. Code Relatives: Detecting similarly
Behaving Software. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2016).
ACM, 702-714.
40. RQ 1. Comparison with Code-to-Code
Search Engines
• Query tools with top 10 Stackoverflow snippets
• Manual checking
• Does FaCoY find similar code fragments?
• Syntactically or Semantically?
26
41. RQ 2. Benchmark Assessment
• IJaDataset 2.0 of 25,000 projects (> 3 M files)
• BigCloneBench annotates 8,345,104 clone pairs with 43 functionalities
• Can FaCoY find more semantic clones (MT3 or T4) than the state-of-the-art?
No initial user query
No query alternation
No query alternation &
No query structuring
27
42. Clone Types
T1 T2 VST3 ST3 MT3 WT3/T4 Total
# of samples 10 10 10 10 10 10 60
BigCloneBench
missed to include
4 1 2 25 32
• For the rest of the 28 files, FaCoY points 26 files correctly,
• But failed to locate in the files.
• In only 2 cases, FaCoY completely failed.
FaCoY can detect clones that BigCloneBench missed.
Double Checking FaCoY’s False Positives
28
43. F18: Play Sound
F19: Take Screenshot to File
F21: XMPP Send Message
FaCoY’s Limitation
Requiring external
APIs and libraries
Pure computation tasks
F7: Bubble Sort Array
F14: Binary Search
F41: Transpose a Matrix
29
FaCoY performs much better with code that are
requiring external APIs and Libraries.
44. RQ 3.Validating Semantic Clones
• Use DyCLINK - A dynamic approach that computes similarity of execution traces to detect code
relatives.
• Index the benchmark of DyCLINK: 411 pairs as code relatives.
• Results:
•FaCoY’s hit ratio is at 68% (278 out of 411 code fragments)
•FaCoY’s Mean Reciprocal Rank value is 0.18 (retrieves into lower rankings)
Google Code Jam
- 2011: Irregular Cake
- 2012: Perfect Game
- 2013: Cheaters
- 2014: Magical Tour
30
45. RQ 4. Recommending code for patches
Buggy code
• Buggy information
Project: Apache commons-lang
File path: projects/Lang/14/org/apache/commons/lang3/StringUtils.java
As maintainers, can we quickly find alternative implementations?
31
46. RQ 4. Recommending code for patches
Buggy code
Additionally, we consider more cases:
1. Are cs1 and cs2 nulls?
2. Are cs1 and cs2 have the same values?
3. Are cs1 and cs2 the same objects?
4. Are cs1 and cs2 are String objects?
5. If they are not pure String objects, check the equality character by character
32
47. RQ 4. Recommending code for patches
Fix
Buggy code
• Commit: cf7211f9 by Matthew Jason Benson, 01/23/2012 06:47 PM
• parent: c8afaa3e
• git-svn-id: https://svn.apache.org/repos/asf/commons/proper/lang/trunk@1234915
• Log: [LANG-786] StringUtils equals() relies on undefined behavior; thanks to Daniel Trebbien
33
48. RQ 4. Recommending code for patches
• 395 bugs in Defect4J repair benchmark [@ISSTA14]
• 21 FaCoY recommendations are correct (manual assessment)
Fix
Buggy code
34