Credits : Yourstory

Website building has become more important than ever before for businesses, thanks to more and more people getting access to the internet. Two of the core elements that are necessary for the website building process are website design and website development. Over the last decade, many people have been left confused by how these two terms are used. As many agencies use them interchangeably, it can be hard for laymen to understand the differences between the two terms. For example, if you have plans of starting your Ecommerce website, you may be wondering if you should hire an agency for Ecommerce website design in India or web development. However, the fact is that there are several differences and we will be going through each one of them in detail. The Basic Difference At the core, web developers and designers deal with two distinct elements of a website. While web designers focus on the visual side of things to ensure that the website looks fantastic, web developers write the code that brings the work of the designers to life. In simple terms, it can be said that website development is not possible without website design. It is only when the developers have the design available that they can start to do their coding work. Roles and Responsibilities In this section, we will be taking a look at the roles and responsibilities of the developers and designers working in a web design and development company in India. Let’s start by talking about the role played by web designers: Consulting and Following Up with Clients: Web designers have to first talk in detail with clients to know what they have in mind. After the consultation, designers have to use their creativity to come up with design elements that incorporate the client’s ideas as well. Following up is also an important part of a web designer’s job. Establishing Brand Identity and a Common Theme across All Pages: One of the major duties of a web designer is to establish brand identity through the use of graphics, fonts, themes, and colours. It is also vital for designers to maintain a common theme across all the pages of a website, as this ensures that the entire website is presented to viewers and end-users as one cohesive unit. Making Web Pages Richer with Features: A website that looks good is okay, but a website that looks good and has a variety of features and functionalities has more chances of being successful. It is the job of the designers to incorporate additional features such as videos and links to different social media pages into the website.

Now let’s take a look at the role of the developers in the best website development company in India: Using Coding Languages to Bring the Design to Life: Once the work of the web designers is done and dusted, it becomes the work of the web developers to use coding languages such as PHP, CSS, HTML, and JavaScript to ‘write’ the website to give life to the website design. Making Websites Trouble-Free: There’s a lot of trial and error work involved in website development as creating a website that looks good and is fast and responsive can take time. The developers must figure out what the existing loopholes are on a website and to fix them so that the final product is something that impresses not just the client, but the target audience as well. Monitoring Traffic: Once the website goes live, it is up to the developers to monitor website traffic to understand how well the website is able to fare in terms of engagement with users. If the engagement is low, developers can make further changes to the website. So now that you know how designers and developers work differently on various elements of a website, you should be able to hire a web development or website design agency in India depending on your needs.

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Software Development AI Market is expected to witness market growth at a rate of 42.30% in the forecast period of 2020 to 2027. New Growth Forecast Report on Global Software Development AI Market, By Programming Language (Python, R, Lisp, Prolog, Java, and Others), Development Phases (Planning, Knowledge Acquisition and Analysis, and System Evaluation), Approaches (Natural Language Processing Techniques, Neural Networks, Fuzzy Logic, Ant Colony Optimization (ACO), General Algorithm, Tabu Search, Bee Colony, Data Mining, and Others), Application (Expert System, Project Management, and Others), Country (U.S., Canada, Mexico, Brazil, Argentina, Rest of South America, Germany, Italy, U.K., France, Spain, Netherlands, Belgium, Switzerland, Turkey, Russia, Rest of Europe, Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific, Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa) Industry Trends and Forecast to 2027

‘Global Software Development AI Market Research Report’, the report is complete with an elaborate research undertaken by prominent analysts and a detailed analysis of the global industry place. Software Development AI Market is having several developments, product launches, joint ventures, merges and accusations by its prominent market players and top brands which are driving the market in the terms of sales, import, export and revenue. This report gives an edge to not just compete but to outmatch the competition. The report provides with CAGR value fluctuation during the forecast period of 2020-2027 for the market. This report also contains all the drivers and restrains with the help of SWOT analysis for the market.

“Product definition” The increase in the fraction of novel start-ups and improvements in technology has directed to an expansion in expenditure in AI technologies. Furthermore, an increase in requirement for interpreting and representing massive volumes of data is increasing the need for artificial intelligence software development for enterprise solutions. Moreover, the advancement of trustworthy cloud computing foundations and developments in effective artificial intelligence has made an influential impression on the germination potential of the AI market. Nevertheless, the shortage of skilled and qualified instructors can limit the increment of the AI software development business.

Competitive Landscape Software development AI market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, regional presence, company strengths and weaknesses, product launch, product width and breadth, application dominance. The above data points provided are only related to the companies’ focus related to software development AI market. Future Outlook and by Top key players Analysis IBM Corporation, Alphabet Inc., Microsoft, Facebook, SAP SE, Salesforce.com, Inc., Fair Isaac Corporation, BoardBookit, Inc., Intel Corporation, NVIDIA Corporation, Anki, AIBrain Inc., Banjo, Apple Inc., Amazon.com, Inc., Appier Inc., SenseTime, Kindred, Inc., and OrCam among other 

Global Software Development AI Market: Segment Analysis

Global Software Development AI Market, By Programming Language (Python, R, Lisp, Prolog, Java, and Others), Development Phases (Planning, Knowledge Acquisition and Analysis, and System Evaluation), Approaches (Natural Language Processing Techniques, Neural Networks, Fuzzy Logic, Ant Colony Optimization (ACO), General Algorithm, Tabu Search, Bee Colony, Data Mining, and Others), Application (Expert System, Project Management, and Others), Country (U.S., Canada, Mexico, Brazil, Argentina, Rest of South America, Germany, Italy, U.K., France, Spain, Netherlands, Belgium, Switzerland, Turkey, Russia, Rest of Europe, Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific, Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa) Industry Trends and Forecast to 2027

Global Software Development AI Market Scope and Market Size

Software development AI market is segmented on the basis of programming language, development phases, approaches, and application. The growth among segments helps you analyse niche pockets of growth and strategies to approach the market and determine your core application areas and the difference in your target markets.

  • On the basis of programming language, the software development AI market is segmented into python, R, lisp, prolog, java, and others.
  • On the basis of approaches, the software development AI market is segmented into natural language processing techniques, neural networks, fuzzy logic, ant colony optimization (ACO), general algorithm, tabu search, bee colony, data mining, and others.
  • On the basis of development phases, the software development AI market is segmented into planning, knowledge acquisition and analysis, and system evaluation.
  • On the basis of application, the software development AI market is segmented into expert system, project management, and others.

Key Highlights from Software Development AI Market Study

Revenue and Sales Estimation — Historical Revenue and sales volume is presented and further data is triangulated with top-down and bottom-up approaches to forecast complete market size and to estimate forecast numbers for key regions covered in the report along with classified and well recognized Types and end-use industry. Additionally macroeconomic factor and regulatory policies are ascertained in Software Development AI industry evolution and predictive analysis.

Manufacturing Analysis —the report is currently analyzed concerning various product type and application. The Software Development AI market provides a chapter highlighting manufacturing process analysis validated via primary information collected through Industry experts and Key officials of profiled companies.

Competition — Leading players have been studied depending on their company profile, product portfolio, capacity, product/service price, sales, and cost/profit.

Demand & Supply and Effectiveness — Software Development AI report additionally provides distribution, Production, Consumption & EXIM** (Export & Import). ** If applicable

In addition, the years considered for the study are as follows:

Historical year – 2014-2019   |    Base year – 2019   |    Forecast period – 2020 to 2027

Table of Content: Global Software Development AI  Market Research Report 2020-2027

Chapter 1: Software Development AI Market Overview
Chapter 2: Software Development AI Market Economic Impact
Chapter 3: Competition by Manufacturer
Chapter 4: Production, Revenue (Value) by Region (2020-2027)
Chapter 5: Supply (Production), Consumption, Export, Import by Regions (2020-2027)
Chapter 6: Production, Revenue (Value), Price Trend by Type
Chapter 7: Software Development AI Market Analysis by Application
Chapter 8: Software Development AI Market by Manufacturing Cost Analysis
Chapter 9: Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10: Software Development AI Marketing Strategy Analysis, Distributors/Traders
Chapter 11: Software Development AI  Market Effect Factors Analysis
Chapter 12: Software Development AI Market Forecast (2020-2027)
Chapter 13: Appendix

Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe, MEA or Asia Pacific.

Key questions answered in the Global Software Development AI Market report include:

  • What will be Software Development AI market share and the forecast for 2020-2027?
  • What are the key factors compelling the worldwide Software Development AI market?
  • Who are the key players in the world Software Development AI industry?
  • What are the factors impacting the revenue and production growth of the Software Development AI market?
  • What are the opportunities & challenges in the Software Development AI industry?

About Data Bridge Market Research:

An absolute way to forecast what future holds is to comprehend the trend today!

Data Bridge set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market. Data Bridge endeavors to provide appropriate solutions to the complex business challenges and initiates an effortless decision-making process.

Data Bridge adepts in creating satisfied clients who reckon upon our services and rely on our hard work with certitude. We are content with our glorious 99.9 % client satisfying rate.

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The IoT in Web Development Market report released and promoted by IT intelligence market draw out historical, existing, and forecast valuation of the IoT in Web Development industry till 2026. The report highlights the market essentials, opportunities, regional market, Emerging Growth Factors, market challenges, forecast and competitors joined with their IoT in Web Development Market share. The fundamental purpose of IoT in Web Development Market report is to provide a appropriate and strategic analysis of the IoT in Web Development industry.

Top merchant analysis is one of the key component and is exceptionally helpful for each player to comprehend focused scene in the market. Major key companies present in Dot Com Infoway Limited, Upwork, GoodFirms.

The Market structure covers the value chain, player categories, product ranges, key players’ presence across products and end user segments of the market. The report also provides a snapshot of key competition, market trends with forecast over the next 5 years, anticipated growth rates and the principal factors driving and impacting growth market data and analytics are derived from a combination of primary and secondary sources.

The research process involved the study of various factors affecting the industry, including the government policy, market situation, competitive landscape, historical data, present trends in the market, technological innovation, upcoming technologies and the technical progress in related industry, and market risks, opportunities, market barriers, and challenges.

IoT in Web Development Market report is the believable source for gaining the market research that will exponentially accelerate your business. Additionally, it Presents new task SWOT examination, speculation attainability investigation, and venture return investigation.

Market Event Factors Analysis

Market driver

  • Increasing IoT in Web Development Market invasion of new technolgies.
  • For a full detailed, view our report
  • Market challenge
  • Stringent regulatory challenges in IoT in Web Development applications.
  • For a full detailed, view our report
  • Market trend
  • Rising demand for IoT in Web Development in market.
  • For a full detailed, view our report

Key questions answered in IoT in Web Development Market 2018 – 2026 report:

  • What will the market size be in 2026 and what will the growth rate be?
  • What are the key market trends?
  • What is key factor driving this market?
  • What are the challenges to market growth?
  • Who are the major key vendors in this market space?
  • What are the market opportunities, market risk and market overview and threats faced by the key vendors?
  • What are the strengths and weaknesses of the key vendors?

In the end IoT in Web Development Market Report delivers conclusion which includes Research Findings, Market Size Estimation, Market Share, Consumer Needs/Customer Preference Change, Data Source. These factors will increase business overall.

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Global Software Development AI Market, By Programming Language (Python, R, Lisp, Prolog, Java, and Others), Development Phases (Planning, Knowledge Acquisition and Analysis, and System Evaluation), Approaches (Natural Language Processing Techniques, Neural Networks, Fuzzy Logic, Ant Colony Optimization (ACO), General Algorithm, Tabu Search, Bee Colony, Data Mining, and Others), Application (Expert System, Project Management, and Others), Country (U.S., Canada, Mexico, Brazil, Argentina, Rest of South America, Germany, Italy, U.K., France, Spain, Netherlands, Belgium, Switzerland, Turkey, Russia, Rest of Europe, Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific, Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa) Industry Trends and Forecast to 2027.

Market Analysis and Insights: Global Software Development AI Market

Software development AI market is expected to witness market growth at a rate of 42.30% in the forecast period of 2020 to 2027. Data Bridge Market Research report on software development AI market provides analysis and insights regarding the various factors expected to be prevalent throughout the forecasted period while providing their impacts on the market’s growth.

The increase in the fraction of novel start-ups and improvements in technology has directed to an expansion in expenditure in AI technologies. Furthermore, an increase in requirement for interpreting and representing massive volumes of data is increasing the need for artificial intelligence software development for enterprise solutions. Moreover, the advancement of trustworthy cloud computing foundations and developments in effective artificial intelligence has made an influential impression on the germination potential of the AI market. Nevertheless, the shortage of skilled and qualified instructors can limit the increment of the AI software development business.

This software development AI market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research software development AI market contact us for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Global Software Development AI Market Scope and Market Size

Software development AI market is segmented on the basis of programming language, development phases, approaches, and application. The growth among segments helps you analyse niche pockets of growth and strategies to approach the market and determine your core application areas and the difference in your target markets.

  • On the basis of programming language, the software development AI market is segmented into python, R, lisp, prolog, java, and others.
  • On the basis of approaches, the software development AI market is segmented into natural language processing techniques, neural networks, fuzzy logic, ant colony optimization (ACO), general algorithm, tabu search, bee colony, data mining, and others.
  • On the basis of development phases, the software development AI market is segmented into planning, knowledge acquisition and analysis, and system evaluation.
  • On the basis of application, the software development AI market is segmented into expert system, project management, and others.

Software Development AI Market Country Level Analysis

Software development AI market is analysed and market size, volume information is provided by country, programming language, development phases, approaches, and application as referenced above.

The countries covered in the market report are the U.S., Canada and Mexico in North America, Brazil, Argentina and Rest of South America as part of South America, Germany, Italy, U.K., France, Spain, Netherlands, Belgium, Switzerland, Turkey, Russia, Rest of Europe in Europe, Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC)  in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA).

North America province offers the highest AI industry division and is forecasted to acquire the preeminent position through the forecast interval, owing to the proximity of essential corporations and huge expenditure in the artificial intelligence market.

The country section of the report also provides individual market impacting factors and changes in regulation in the market domestically that impacts the current and future trends of the market. Data points like down-stream and upstream value chain analysis, technical trends and porter’s five forces analysis, case studies are some of the pointers used to forecast the market scenario for individual countries. Also, the presence and availability of global brands and their challenges faced due to large or scarce competition from local and domestic brands, impact of domestic tariffs and trade routes are considered while providing forecast analysis of the country data.

Competitive Landscape and Software Development AI Market Share Analysis

Software development AI market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, regional presence, company strengths and weaknesses, product launch, product width and breadth, application dominance. The above data points provided are only related to the companies’ focus related to software development AI market.

The major players covered in the software development AI market report are IBM Corporation, Alphabet Inc., Microsoft, Facebook, SAP SE, Salesforce.com, Inc., Fair Isaac Corporation, BoardBookit, Inc., Intel Corporation, NVIDIA Corporation, Anki, AIBrain Inc., Banjo, Apple Inc., Amazon.com, Inc., Appier Inc., SenseTime, Kindred, Inc., and OrCam among other domestic and global players. Market share data is available for global, North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America separately. DBMR analysts understand competitive strengths and provide competitive analysis for each competitor separately.

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This report studies the Application Development Software Market with many aspects of the industry like the market size, market status, market trends and forecast, the report also provides brief information of the competitors and the specific growth opportunities with key market drivers. Find the complete Application Development Software Market analysis segmented by companies, region, type and applications in the report.

The market report aims to make detail analysis and in-depth research on the development environment, market size, share, and development trend. It’s a well-drafted report for those who are eager to know the existing market status at the global level. All contents featured in this report were gathered and validated via extensive research methods such as primary research, secondary research, and SWOT analysis.

Some of the key players Analysis in Application Development Software Market: Atlassian, Docker, GitHub, GitLab, Microsoft Corporation, Odoo, Plesk International, SAP SE, Snappii, Zoho Corporation.

One of the crucial parts of this report comprises Application Development Software industry key vendor’s discussion about the brand’s summary, profiles, market revenue, and financial analysis. The report will help market players build future business strategies and discover worldwide competition.  A detailed segmentation analysis of the market is done on producers, regions, type and applications in the report.

On the basis of geographically, the market report covers data points for multiple geographies such as United States, Europe, China, Japan, Southeast Asia, India, and Central& South America

Analysis of the market:

Other important factors studied in this report include demand and supply dynamics, industry processes, import & export scenario, R&D development activities, and cost structures. Besides, consumption demand and supply figures, cost of production, gross profit margins, and selling price of products are also estimated in this report.

Predominant Questions Answered in This Report Are:

  • Which segments will perform well in the Application Development Software market over the forecasted years?
  • In which markets companies should authorize their presence?
  • What are the forecasted growth rates for the market?
  • What are the long-lasting defects of the industry?
  • How share market changes their values by different manufacturing brands?
  • What are the qualities and shortcomings of the key players?
  • What are the major end results and effects of the five strengths study of industry?

The conclusion part of their report focuses on the existing competitive analysis of the market.  We have added some useful insights for both industries and clients. All leading manufacturers included in this report take care of expanding operations in regions. Here, we express our acknowledgment for the support and assistance from the Application Development Software industry experts and publicizing engineers as well as the examination group’s survey and conventions. Market rate, volume, income, demand and supply data are also examined.

Table of contents:

Application Development Software Global Market Research Report 2020

1 Market Overview

2 Manufacturers Profiles

3 Global Application Development Software Sales, Revenue, Market Share and Competition by Manufacturer

4 Global Application Development Software Market Analysis by Regions

5 North America Application Development Software by Country

6 Europe Application Development Software by Country

7 Asia-Pacific Application Development Software by Country

8 South America Application Development Software by Country

9 Middle East and Africa Application Development Software by Countries

10 Global Application Development Software Market Segment by Type

11 Global Application Development Software Market Segment by Application

12 Application Development Software Market Forecast (2020-2024)

13 Sales Channel, Distributors, Traders and Dealers

14 Research Findings and Conclusion

15 Appendix

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Credits : Techengage

Web development is a versatile field offering undergrads plenty of job options. But many students don’t know how to start. It seems to them that employers don’t hire people without experience or that competition in the IT industry is too high. So they often become discouraged and convince themselves they can’t make the running.

But let’s see what prospects await you. There is a growing demand for web developers. It’s driven by information technology development, adoption of e-commerce, and virtual reality technology. According to the experts, web developers’ employment is projected to increase by 13 percent from 2018 to 2028, which is much higher than the average for all professions. So employers are willing to hire specialists even with little to no experience. And your task is to be a promising candidate they’re looking for.

So, where to begin?

Learn and practice

Start with the basics. A good web developer should have mastered HTML, CSS, and JavaScript. Besides, they should have experience with CSS frameworks like Bootstrap, Backbone, and Foundation and be familiar with back-end languages like Ruby, Java, and PHP.

Good if you are already studying these fundamentals at a college or university. If not, sign up for a few programming courses at online learning platforms like Codeacademy, Udacity, W3Schools, etc. But remember that getting a degree in computer science isn’t an essential requirement to obtain a job in IT, but it will definitely give you a competitive advantage. Try to enhance your skills through practice and experience, and to make sure you have enough time for that.

Having a lot of practice is indispensable at this stage. Gain experience by building applications or websites, tinkering around it until it turns into something worthwhile and functional. Your work might not look like the greatest website in the world, but if you get the site working, it’s a huge accomplishment.

Have projects to show off your skills

When you’ve got enough practice under your belt, it’s time to demonstrate your skills. The creation of a personal project can become a crucial factor contributing to your success.

Put yourself in an employer’s shoes. You’re coming in with little to no experience, so how will the company know that you can actually do the work, and you’re not just wasting their time? The answer lies in a well-developed, and elaborate personal project. It’ll not only show what you’re capable of but also tell that you’re passionate about the thing you do. Otherwise, you wouldn’t have invested so much time into a personal project unless you really enjoyed coding. In addition, it’ll show that you can work on your own. So, work on your own project and make sure it looks professional. Because depending on its quality, your employer will draw a conclusion on how you’re going to code on their team.

Create a unique CV

The next advice is pretty obvious, but it works 100%. Write a CV that really stands out from thousands of other impersonal pieces of paper. The last thing you want is to type it on a blank, boring document and use generic business jargon, right? An eye-catching and thought-out CV is of vital importance when applying for a job in major companies. They receive a huge number of CVs every day, and you don’t want yours to be missed out on, right?

To enliven your CV, use one of the pre-made themes in Microsoft Word. Add some details and recommendations from your teachers, professors, and past employers if you have any, but not overdo it. A good rule of thumb is the more specific your CV is to the position that you’re applying for, the better.

Don’t stop applying

It is probably the key step, the point where all the magic happens. Everything you’ve done before was about setting yourself up for success in this stage. And now, when you’ve got a high-end personal project, and a catchy CV, you can take action. Don’t expect to get your dream job after applying for five or ten positions. Then you’ll simply get disappointed when being rejected or ignored.

So how many applications should you send? Perhaps this number will reach 100 or 200, or even more. Before wondering how long will it take to land a job, look at the situation from a different angle. Assume that 1 out of every 30 applications will get a response. That means that the more applications you send, the quicker you’ll get those enough responses, and your dream job. As someone once said, if opportunities don’t knock, build a door.

So instead of just dreading rejection where you don’t hear back from an employer, think of it as you’re only one step closer to getting the job that you’d love.

Take risks and any chances

Don’t think that you are not qualified enough for that big company or that they won’t hire you because of your young age. Try, and you’ll get the answer! Strive to work for the exact human being that you aspire to be. If you want to work for Google or Samsung, apply for the position you want. Don’t waste your time. If getting a refusal, get back to the previous tip. Take risks if you know that it can lead you to success. Start working, take some freelance projects, maybe for free, or for the minimum salary at the beginning, and you’ll gradually grow as an expert web developer.

To draw the line

Companies want employees who can code and build great sites and apps, and how or where they’re studying is a secondary question. It really is. Your goal is to show your skills and strengths competently. And don’t be disturbed by tight competition in the IT industry. The number of potential employees corresponds to the number of vacancies so everyone will find their place. Dedicate time to developing yourself, and you’ll achieve success in the IT industry.

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Credits : Automotiveworld

Experts from Synopsys discuss the challenges and security risks of connected and autonomous vehicles (CAV), with a particular focus on high-risk areas such as wireless connectivity and autonomous driving functionality. To address these challenges, Synopsys presents several solutions for secure software development and testing, which allow for the finding and fixing of bugs and vulnerabilities earlier in the software development lifecycle.

Key topics and takeaways:

  • Understanding of high-risk areas for CAVs
  • Examples of relevant vulnerabilities and threats
  • Solutions for secure software development and testing
  • How to find vulnerabilities earlier in the software development lifecycle
  • How to reduce security risks in CAVs before production

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Credits : Spectrum.ieee

If you’re a newly hired software engineer, setting up your development environment can be tedious. If you’re lucky, your company will have a documented, step-by-step process to follow. But this still doesn’t guarantee you’ll be up and running in no time. When you’re tasked with updating your environment, you’ll go through the same time-consuming process. With different platforms, tools, versions, and dependencies to grapple with, you’ll likely encounter bumps along the way.

Austin-based startup Coder aims to ease this process by bringing development environments to the cloud. “We grew up in a time where [Microsoft] Word documents changed to Google Docs. We were curious why this wasn’t happening for software engineers,” says John A. Entwistle, who founded Coder along with Ammar Bandukwala and Kyle Carberry in 2017. “We thought that if you could move the development environment to the cloud, there would be all sorts of cool workflow benefits.”

With Coder, software engineers access a preconfigured development environment on a browser using any device, instead of launching an integrated development environment installed on their computers. This convenience allows developers to learn a new code base more quickly and start writing code right away. It also makes it easier to update the different components of a development environment, maintaining consistency across the team. Moreover, this setup could benefit companies shifting to a remote workforce, especially with the COVID-19 pandemic forcing people to work from home.

Because Coder’s development environments run in the cloud, software engineers can take advantage of more processing power to perform intensive computing operations. “Even doing something as simple as cloning a repo[sitory] happens in a matter of seconds because you’re using the cloud network rather than your local Internet connection,” Entwistle says.

Yet cloud-based platforms have their limitations, the most crucial of which is they require reliable Internet service. “We have support for intermittent connections, so if you lose connection for a few seconds, you don’t lose everything. But you do need access to the Internet,” says Entwistle. There’s also the task of setting up and configuring your team’s development environment before getting started on Coder, but once that’s done, you can share your predefined environment with the team.

To ensure security, all source code and related development activities are hosted on a company’s infrastructure—Coder doesn’t host any data. Organizations can deploy Coder on their private servers or on cloud computing platforms such as Amazon Web Services or Google Cloud Platform. This option could be advantageous for banks, defense organizations, and other companies handling sensitive data. In fact, one of Coder’s customers is the U.S. Air Force, and the startup closed a US $30 million Series B funding round last month (bringing its total funding to $43 million), with In-Q-Tel, a venture capital firm with ties to the U.S. Central Intelligence Agency, as one of its backers.

“We’re a solution that helps [the U.S. Air Force] keep security measures in place while also enabling their engineers to be more productive,” Entwistle says. “All development is done on their infrastructure, which means there’s no source code on computers and an engineer’s laptop is no longer part of the cyberattack surface. Because of the way we’re deployed and the benefits of moving sensitive intellectual property away from the end user, we’re a good fit.”

For future releases, Coder is looking to expand into data science and add more features to support collaboration among teams. But their main focus will always be bringing software development to the cloud. “We want to remove the friction an engineer experiences so they can get back to doing what they love—which is to write code,” says Entwistle.

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Credits : Eletimes

With the rise of Artificial Intelligence (AI), Machine Learning (ML) and the Internet of Things (IoT), applications are moving to the network edge where data is collected, requiring power-efficient solutions to deliver more computational performance in ever smaller, thermally constrained form factors. Through its Smart Embedded Vision initiative, Microchip Technology Inc. (Nasdaq: MCHP) is meeting the growing need for power-efficient inferencing in edge applications by making it easier for software developers to implement their algorithms in PolarFire® field-programmable gate arrays (FPGAs). As a significant addition to the solutions portfolio in this segment, Microchip’s VectorBlox Accelerator Software Development Kit (SDK) helps developers take advantage of Microchip’s PolarFire FPGAs for creating low-power, flexible overlay-based neural network applications without learning an FPGA tool flow.

FPGAs are ideal for edge AI applications, such as inferencing in power-constrained compute environments, because they can perform more giga operations per second (GOPS) with greater power efficiency than a central processing unit (CPU) or graphics processing unit (GPU), but they require specialized hardware design skills. Microchip’s VectorBlox Accelerator SDK is designed to enable developers to code in C/C++ and program power-efficient neural networks without prior FPGA design experience.

The highly flexible tool kit can execute models in TensorFlow and the open neural network exchange (ONNX) format which offers the widest framework interoperability. ONNX supports many frameworks such as Caffe2, MXNet, PyTorch, and MATLAB®. Unlike alternative FPGA solutions, Microchip’s VectorBlox Accelerator SDK is supported on Linux® and Windows® operating systems, and it also includes a bit accurate simulator which provides the user the opportunity to validate the accuracy of the hardware while in the software environment. The neural network IP included with the kit also supports the ability to load different network models at run time.

“In order for software developers to benefit from the power efficiencies of FPGAs, we need to remove the impediment of them having to learn new FPGA architectures and proprietary tool flows, while giving them the flexibility to port multi-framework and multi-network solutions,” said Bruce Weyer, vice president of the Field Programmable Gate Array business unit at Microchip. “Microchip’s VectorBlox Accelerator SDK and neural network IP core will give both software and hardware developers a way to implement an extremely flexible overlay convolutional neural network architecture on PolarFire FPGAs, from which they can then more easily construct and implement their AI-enabled edge systems that have best-in-class form factors, thermals and power characteristics.”

For inferencing at the edge, PolarFire FPGAs deliver up to 50 percent lower total power than competing devices, while also offering 25 percent higher-capacity math blocks that can deliver up to 1.5 tera operations per second (TOPS). By using FPGAs, developers also have greater opportunities for customization and differentiation through the devices’ inherent upgradability and ability to integrate functions on a single chip. The PolarFire FPGA neural network IP is available in a range of sizes to match the performance, power, and package size tradeoffs for the application, enabling customers to implement their solutions in package sizes as small as 11 × 11 mm.

Microchip’s Smart Embedded Vision initiative was launched last July to provide hardware and software developers with tools, intellectual property (IP) cores, and boards for meeting the thermally constrained and small-form-factor requirements of edge applications. Because PolarFire FPGAs deliver lower power compared to other solutions, customers can eliminate the need for fans in their enclosures. PolarFire FPGAs also offer more functional integration for a customer’s design. For example, in applications such as a smart camera, PolarFire FPGAs can integrate the image signal pipeline which includes the sensor interface, DDR controller, image signal processing (ISP) IP and network interfaces, all while integrating the machine learning inference.

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Credits : E3zine

One of the most discussed software architectures are microservices. In other words, the request to split the monolithic applications of the past into small, autonomous services. However, there are some issues with microservices: They are expensive – as they are required for unlimited scaling – and only work if the underlying culture is embraced in its entirety.

This raises multiple questions. For us in the ERP world, is unlimited scaling a requirement? Also, our applications are expensive enough and a complete shift of the whole application towards a different architecture is often impractical. Nevertheless, SAP has a couple of offerings built on microservices, and we can learn from these examples.

Microservices are autonomous

At its core, the microservice architecture requires each service to be fully autonomous. It has a well-defined network API to get data in and out, no other methods allowed. The decision of which technologies to use to implement the service is up to the responsible team. Sounds simple enough, but the decision has consequences.

A sales order service is trivial in the first cut. The service exposes an API to create, modify and change sales orders. All sales orders are persisted in a database with the sales order header and item table. It isn’t rocket science, right?

A sales order should reference existing customers and material master data, but these are other services and hence not part of our database’s tables. But if they are not part of this database, all features that a database provides for free, including transactions, enforcing foreign key constraints, fast joins and the like, need to be re-implemented on service level.

The obvious solution would be to make the database with its tables one service, and the business logic where sales orders are maintained would be another microservice. Something like a 3-layer service architecture with the UI layer, the application layer and the database layer. The application layer exposes function modules for the various operations and …. Did we just re-invent the R/3 architecture?

When using a single database to store all data, the data model is the monolith. As the data model is the core, a huge part of the application remains monolithic. Including all the negative side effects, like changing the table structures requires all other modules to be analyzed if they will be impacted. Changes need to be applied in the same manner.

The result is the opposite of autonomous. Calling that a microservice instead of a client-server architecture is purely semantic, I would argue.

Eventing and eventual consistency

The software industry’s answer to that problem is to use events as the information backbone. Every service broadcasts all changes made to the data and the interested parties can listen and persist the required data in their own database.

In the example of the sales order database, it would have tables with material master data and business partner records for reference, except that these extra tables cannot be modified. When the material service broadcasts the event, effectively saying, “There is a new material with the following data”, it is modifying the local reference table.

As every event has some delay, it might happen that the user created a new material record and ordered that material immediately after. The order service will get the material master event in a few milliseconds, but triggered right now it returns the error, “Material does not exist”.

Another issue is scaling within the service. In an optimally designed microservices architecture, the performance can be increased linear by starting more instances of the same service. For business logic, this is doable, but the database is the problem.

If all sales order services use the same database, they only scale as much as the weakest link. In a properly designed microservices architecture, each service instance has its own database and it is kept up to date via distributing the change events.

Another downside of each service having its own reference data is the size of the microservices. With every iteration the service requires more reference data until it is a copy of a large portion of the entire ERP system. Just the opposite of SAPs theme of ‘no aggregates, no data duplication’.

In my opinion, it was the right step for the ERP development team to resist embracing a microservices architecture in S/4 Hana.

API-driven architecture

Other properties of a microservices architecture include:

  • well defined APIs;
  • version tolerance of the APIs;
  • modularity;
  • and fault tolerance.

Using those as arguments to favor microservices is reversing cause and effect. Coming back to the R/3 architecture, the SAP ERP system has well-defined APIs to create sales orders, to read material master, and so on. Many of them have additional optional parameters, some exist in different versions. For sure the application is modular. With the client server architecture, there is at least some fault tolerance on application and presentation layers.

Does that make the R/3 architecture a microservices architecture? Of course not, it is just a properly built solution. These requirements are simply not exclusive to microservices.

SAP Cloud Platform’s microservices architecture

One area where SAP is following the microservices architecture is SCP.

Each service deployed is an isolated entity, publishes its APIs in a central registry – the API Hub – and multiple instances can be started to increase throughput.

The CAPM supports even eventing of services but ignores out of the box support of distributing change data across the database instances. SCP does not need that, as the database is used as a service as in a client server model and is not an intrinsic part of the service itself.

One way to look at it is that it combines the worst of both worlds: It scales like a client server architecture and adds the complexities of microservices. Security is maintained outside of the database, services need various bindings, services require defined routes, every service must serialize and deserialize the payload for data exchange, every service needs to reevaluate the user security, etc. In brief, simple things are tedious, complex tasks are extremely difficult.

SAP Data Quality microservices

Another example are the SAP EIM Data Quality services to validate and cleanse various aspects of address data.

For such an offering, a microservice architecture is perfect. The required address reference data is part of each service instance. There is no need to sync the data across instances due to the static of the postal data. The user sends a http request with an in-doubt address payload to the service and gets back the corrected address, the information on what has been corrected, and the confidence level. It scales perfectly and the overhead is little.

Summary

Microservices have two use cases as sweet spots. If unlimited scaling is required because the service might grow into millions of requests per hours, there is not much choice and a microservice architecture must be chosen, regardless of costs. If the desired service is stateless (does neither make changes in a database nor relies on a global application consistency), it will be autonomous anyhow. In that case, the result will be something microservice-like and then it makes sense to add the few additional concepts, e.g. documenting the API, versioning the API, etc.

For database applications, especially when one instance is used for a foreseeable number of users only, a microservices architecture is questionable.

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