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Symantec ST0-248 : Symantec Storage Foundation 6.1 for UNIX Technical Assessment Exam

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Exam Number : ST0-248
Exam Name : Symantec Storage Foundation 6.1 for UNIX Technical Assessment
Vendor Name : Symantec
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Symantec UNIX certification

IBM CodeNet: synthetic Intelligence that may program computers And remedy A $one hundred Billion Legacy Code issue | ST0-248 Practice Test and real questions

notion for AI for Code

IBM

desktop scientists have long toyed with the conception of creating computers that could write classes for different computer systems. artificial intelligence is an obvious technology for the assignment. It has been prior to now used for programming on a small scale but regrettably the effects were restrained.

synthetic intelligence is one in all our strongest and versatile technologies in use today. it might probably bear in mind and generate speech, analyze documents, recognize images and characters, power automobiles, pilot struggle planes, write papers, and function hundreds of different effective operations. 

Dr. Ruchir Puri is an IBM fellow and Chief Scientist for IBM research. For the previous two and a half years, Dr. Puri and a crew from IBM analysis and the MIT-IBM Watson AI Lab have worked on a massive code-intensive AI for Code task.  At this year’s IBM suppose 2021 convention, Arvind Krishna, Chairman and CEO of IBM, published the outcomes of that two-yr effort and introduced a superb piece of analysis referred to as undertaking CodeNet - a large dataset aimed toward teaching AI to code.

a large fraction (over 50%) of the code samples utilized in CodeNet are ordinary to assemble and run ... [+] as it should be on the prescribed verify instances. supply: "venture CodeNet: a big-Scale AI for Code Dataset for learning a range of Coding projects," with the aid of Ruchir Puri et al. 2021 can also 25, https://arxiv.org/abs/2105.12655v1

IBM

CodeNet is similarly named after ImageNet, a groundbreaking AI for computer imaginative and prescient dataset. ImageNet carries over 14 million pictures scattered across 20,000 classes. ImageNet is the leading cause of ingenious progress that has been made in AI for computer imaginative and prescient algorithms and compute.  CodeNet dataset likewise has the capability to drive innovation and increase in future AI for Code items.

CodeNet is remarkable

A assessment of CodeNet with two conventional and an identical datasets supply: Ruchir Puri et al. 2021 ... [+] might also 25, https://arxiv.org/abs/2105.12655v1

IBM

There are a number of available datasets for supply code, however most are most effective advantageous for a couple of focused projects. Two everyday datasets, GCJ and POJ, are compared to CodeNet within the above chart. GCJ become collected from submissions to Google's coding competitors from 2008 to 2020. POJ-104 uses code samples amassed from an academic web site designed for programming competitions. whereas these datasets are narrow in scope, CodeNet is designed for large and comprehensive use.

CodeNet is unheard of. it's a huge-scale open-supply dataset with 14 million code samples and 500 million traces of code. CodeNet additionally has over 4,000 difficulty descriptions and 55 programming languages, from contemporary ones like C++, Java, Python, and Go to legacy languages like COBOL, Pascal, and FORTRAN. 

in addition to its prosperous range of languages and complications, eighty% of the issues have over 100 solutions each. IBM has annotated the code samples with a wealthy collection of tips, comparable to code size, reminiscence footprint, CPU run time, and status, indicating acceptance or error varieties. 

Modernizing our utility infrastructure is primary.

Cobol

IBM

IBM’s efforts to Improve AI for Code is part of a bigger aim. there is an urgent need to modernize legacy utility infrastructure infused all over the whole lot of global business and government. a huge component of nowadays’s business code originated from multi-yr efforts of programming groups whose individual memberships fluctuated over time. in consequence, a various volume of thought and planning went into building massive monoliths of code that now exist as legacy software. For those explanations, it's complicated for these days’s developers to modernize legacy code or make alterations to enhance performance, accommodate new features, or realign it for brand new regulations.

COBOL, a legacy giant 

there are lots of programs written in a legacy programming language called COBOL (an acronym for "typical business-oriented language").  COBOL became developed and closely promoted with the aid of the U.S. branch of defense in the early Nineteen Sixties. through the years, it has grown into an trade normal. It continues to be used in many enterprise purposes, banking transactions and government administrative mainframes.  

because it is so generic, manually upgrading COBOL courses could be a significant accomplishing in human effort, funds, and scope. COBOL turned into once considered an superior programming language. besides the fact that children, as it is a large a part of our out of date digital infrastructure, it is time for COBOL courses to be modernized.

essential issue commentary, however an incredible task 

The issue is that a huge legacy base of COBOL ought to be migrated to new platforms and rewritten into more modern programming languages. It’s a big project as a result of IBM estimates about 200 to 220 billion strains of legacy COBOL code needs to be rewritten.  on the cost of 32 to 50 cents a coded line using human materials, the modernization of COBOL represents a $one hundred billion problem. How lengthy it might take for an entire rewrite using human programmers? That has yet to be described. 

The difficulty is further complex as a result of COBOL is much less familiar than it once turned into. Many professional COBOL coders have already retired or are about to retire. The extent of a mentioned scarcity in skilled COBOL programmers first grew to be evident at the start of Covid-19. 

Dr. Puri said, “Nothing introduced the scarcity of COBOL programmers to the forefront greater than the pandemic. we are nevertheless within the middle of a pandemic in lots of materials of the world.  happily, in the united states we're beginning to turn the corner, but you nonetheless see many headlines that COBOL coders are needed for the Corona virus combat.”

Dr. Puri also cited one more delicate but significant ingredient that affects the availability of professional COBOL programmers. He mentioned people getting into the application engineering construction area decide upon now not to work on COBOL projects. they'd somewhat work with new languages instead.

One step at a time

Translating legacy code into a new language is complicated. Complexity stems from the contextual nature of programming languages. Most languages use statements to direct operations to be carried out by using the laptop. every statement is carried out inside the nuanced context of a set of situations mandatory to perform the operation. Context in human language may additionally lengthen to a couple of paragraphs, but for code translations, context may be scattered across numerous libraries. because of this, deciding upon context is complex for artificial intelligence. furthermore, the better the application, the extra complicated the translation becomes. 

CodeNet comes with a collection of developer tools and artificial intelligence fashions for code understanding a good way to proceed to be enhanced with more suitable algorithms to extract context and translate from a legacy code to a contemporary language. For this, CodeNet has pattern input and output check units for over 7 million code samples. 

Early success proves CodeNet’s capabilities and value 

The IBM web site highlights an early instance of how CodeNet become used to modernize legacy code:

“as an instance, a huge car customer approached IBM to assist update a $200 million asset which includes 3,500, multi-technology Java information. These data consisted of more than a million strains of code, developed over a decade with distinct generations of Java know-how.

It turned into a fancy monolithic application code, no longer conducive to cloud-primarily based environments. by using applying our AI for Code stack, we reduced the company's year-lengthy ongoing code migration process all the way down to simply 4 weeks, modernizing and producing over 25 new cloud-native microservices via refactoring the legacy monolithic software code."

near-term knowledge

according to Dr. Puri, CodeNet dataset can permit AI breakthroughs with a view to make developers greater productive and make a contribution to the evolution of AI for Code that promotes the advent of ingenious algorithms. “These capabilities all relate to AI’s capacity to take note code,” he referred to. “In a wide experience, over time, AI will make developers greater productive by coping with routine initiatives they now do on an everyday basis.”

in the future, he says AI for Code will evolve just like the course taken by means of AI for human language realizing, however in this case it'll build upon the wealthy physique of potential in program evaluation. applied sciences might be developed that transcend the variety of programming languages via reasoning concerning the underlying handle and records circulation representations of the code. instead of purely executing working statements, synthetic intelligence will become more constructive as it acquires a deeper realizing of the essence of a programming language.  furthermore, as AI begins to tackle further and further of the events every day tasks, builders will stream up to the subsequent degree and commit greater time to growing new ideas and algorithms.  

here's what Dr. Puri believes application construction will seem like using breakthroughs enabled by means of CodeNet and its AI tools:

  • If a code is not functional, AI will analyze the code and provide recommendations that the developer can use to make it functional, given the constraints of examine information.
  • If the code doesn't run quickly sufficient, then AI will supply suggestions for bigger performance to make the code run sooner.
  • AI will have the ability to increase a code’s reminiscence consumption. If code consumes too a whole lot memory, AI will deliver recommendations to reduce the code’s memory footprint. 
  • AI can look for equivalent codes. It will not be constrained to searching within the language of the application being coded. it might probably additionally search for equivalent code across varied languages.  
  • Breakthroughs enabled with the aid of CodeNet will increase latest program evaluation carry one of the strongest points — developers could be able to translate code into many other languages. 
  • in the future, can CodeNet AI software itself?

    When requested if CodeNet AI can be in a position to application itself, Dr. Puri gave a captivating answer:  

    “I wish to answer that question in two methods,” he mentioned. “I do are expecting CodeNet AI to self-be trained when it comes to advancements it should have. i'd say that may take place in a few years or so. then again, CodeNet and AI for Code is far, far away from developing with new algorithms by itself. I do expect AI to have self-studying where it realizes the weaker ingredients of its practicing and immediately corrects those gaps with the aid of even synthesizing some of the information.  through algorithmic breakthroughs enabled by way of CodeNet, progress in AI for Code should be greatly accelerated, enabling builders to focal point on more artistic programming endeavors.”  

    Analyst Notes

  • it's difficult to overstate CodeNet’s long-time period skills price. it's an incredible milestone for AI for Code. simply as ImageNet modified the trajectory of AI for computing device vision, CodeNet will do likewise for AI for Code.
  • A 2019 IEEE look at confirmed that builders most effective spend forty eight% of their time on genuine code construction. by means of handling routine initiatives, and providing equipment and AI concepts to enhance code, CodeNet will make that 48% tremendously greater productive. 
  • it is vital that IBM developed CodeNet as open source. That assures CodeNet will have group help for continual advancements and new algorithms. it's going to also accelerate adoption which in flip hastens improvements.
  • the usage of CodeNet on scale initiatives will outcome within the introduction of extra ingenious algorithms, which in flip will additional Improve the efficiency of downstream coding tasks. 
  • Given CodeNet’s anticipated effectivity and velocity of translation, as bulk translation of legacy code receives underway, because the project is so giant, it is likely to have a small, but measurable, have an effect on on country wide productivity. 
  • converting legacy code to newer and more manageable code can even have a favorable influence on country wide protection and security as legacy systems are rewritten and changed with faster, extra effective code that has a wider pool of assist substances.
  • by streamlining the process of converting legacy code into up to date language, greater knowledgeable programmers should be available to preserve and replace the translated code as well as create new code.
  • i am expecting many CodeNet analysis papers to be posted in the subsequent six months. I accept as true with CodeNet will create a excessive stage of academic pastime. 
  • observe: Moor Insights & strategy writers and editors may also have contributed to this text. 

    Moor Insights & approach, like every research and analyst establishments, offers or has provided paid analysis, analysis, advising, or consulting to many high-tech corporations in the business, together with 8x8, superior Micro contraptions, Amazon, utilized Micro, ARM, Aruba Networks, AT&T, AWS, A-10 suggestions, Bitfusion, Blaize, box, Broadcom, Calix, Cisco systems, Clear utility, Cloudera, Clumio, Cognitive techniques, CompuCom, Dell, Dell EMC, Dell technologies, Diablo applied sciences, Digital Optics, Dreamchain, Echelon, Ericsson, severe Networks, Flex, Foxconn, frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Google (Nest-Revolve), Google Cloud, HP Inc., Hewlett Packard enterprise, Honeywell, Huawei applied sciences, IBM, Ion VR, Inseego, Infosys, Intel, Interdigital, Jabil Circuit, Konica Minolta, Lattice Semiconductor, Lenovo, Linux groundwork, MapBox, Marvell, Mavenir, Marseille Inc, Mayfair equity, Meraki (Cisco), Mesophere, Microsoft, Mojo Networks, countrywide instruments, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek, Novumind, NVIDIA, Nuvia, ON Semiconductor, ONUG, OpenStack basis, Oracle, Poly, Panasas, Peraso, Pexip, Pixelworks, Plume Design, Poly, Portworx, Pure Storage, Qualcomm, Rackspace, Rambus, Rayvolt E-Bikes, pink Hat, Residio, Samsung Electronics, SAP, SAS, Scale Computing, Schneider electric, Silver height, SONY, Springpath, Spirent, Splunk, dash, Stratus applied sciences, Symantec, Synaptics, Syniverse, Synopsys, Tanium, TE Connectivity, TensTorrent, Tobii know-how, T-cell, Twitter, unity technologies, UiPath, Verizon Communications, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zebra, Zededa, and Zoho which may be noted in blogs and research.




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