Five Prospects for Software Development Technology in 2018
2018 will be a year of tensions and anxieties for developers. On the one hand, the expectations and pressure that new technology products and tools such as block chaining, chatbot, serverless technology, and machine learning will mature to the point where they can be put to practical use, but on the other hand, . However, there will be good news in this pressure.
The year 2018 is expected to open up to developers with tight tug-of-war between pressure on hope for new opportunities and better quality work. Let's see how these opposing forces work and how they will affect developers.
1. Commercialization of B2B transactions using block chains.
Companies are increasingly interested in the strengths of security, reliability, and efficiency of block-chain-based transactions. This year, block-chain applications will emerge in various areas such as financial services and manufacturing supply chain. Block chaining is a technology that enables secure, efficient, and always reliable transactions without intermediaries between institutions that can not guarantee mutual reliability.
Suppose a company orders a particular product from a foreign manufacturer. At this time, the ordered goods are transported through a separate transportation company, after passing through customs, and then arriving at the ordering company through the domestic delivery company. Today, e-mail and spreadsheets demonstrate and coordinate all these processes, and the people and processes required to do so are also significant. On the other hand, if the block chain agrees with the fact that the minimum number of persons concerned is 'proof of this transaction', the existing manual process and adjustment process are omitted by updating the contents of the block chain to permanent records in the block chain.
The block-chain cloud service presents a new perspective on scalability, resiliency and security, and ensures a high degree of pre-built integration with enterprise systems. In addition, it will provide developers with an opportunity to move beyond their hyperlinked fabric implementations and focus more on business applications.
2. Chatbot to actually communicate with customers and employees
As time goes on, people will not want to endure the inconvenience of using multiple apps separately to do the same thing. For example, if you need to use different apps for each airline to check in flights and get tickets. One of the best ways to get rid of these inconveniences is to do this using the messaging features most used on smartphones.
Messaging apps are attractive because they are immediate, expressive, and conversational. In addition, you do not need to be trained for use. Thanks to the development of artificial intelligence and natural language processing technology, it is now possible to ask and answer questions to AI bots using voice assistant technologies such as Facebook Messenger, Slack, Wit Chat, Watts App, or Amazon Alexa or Google Home .
Developers can also use intelligent bot production cloud services to understand the intentions of customers and continue conversations and create bots that maintain integration with backend systems in a short period of time. For example, suppose you want to send a photograph of an actor's clothes seen in a movie to a bots in a clothing store that is frequently visited. The bot will use image recognition technology and AI technology to recommend a similar style of clothes to the user's.
The use of these bots is also good for workers. For example, you can use the bot when you ask for a few days to leave, ask for help at the help desk, or order a replacement laptop. Artificial intelligence bots will recommend the right notebook model for the employee and will update the order status constantly. Developers are more likely to experiment with the bots for the first time when creating the first bot. Failure will be even less of a blow when failing to experiment with other employees in the company.
3. Buttonless world: Interfacing with AI
AI is becoming a new standard in UI. With the advent of this new interface, the existing concurrent request-response model of using apps and services will gradually disappear. Of course, realistic smartphones are still 'low-IQ' tools that users have to pick up, run certain apps, and respond to commands. On the other hand, next generation intelligent apps can initiate their own interaction with users via push notifications. And a step further from here, you will experience artificial intelligence apps, bots, and virtual assistants looking for themselves in a variety of contexts and contexts. Here are two examples.
- Apps that automatically analyze user spend report approval patterns and make decisions on spend decisions reach 99% self-approval rating. The app minimizes the risk of errors by requiring manual approval for exceptional reports that require user review.
- Analytic apps are provided to analyze analysts' existing analytics data, business analysts' question history, and other analysts' queries. The larger the amount of data that an organization collects, the better the level of questions the AI will make about that data.
Developers can now identify and experiment with what data is needed for the business app they want to develop, what perspective they will derive from the transactions they make, and the most valid business decisions based on information derived from these predictive AIs. . Built-in AI automates many of the tasks that are currently being done manually, predicting what the user needs and delivering the right functionality at the right time with the appropriate means.
4. The emergence of practical machine learning apps with expertise in specific areas
Machine learning is now moving from the area of abstract data science in the past to the mainstream application development area. The availability of pre-built modules from popular platforms is increasing, and it has been proven that these modules are very useful when analyzing large data sets. The most valuable and important insights in machine learning technology can be gained when contextual information is available. Contextual information refers to the user's past behavior, previous questions, behavior patterns of others, and distinction between normal and irregular activities.
However, in order for machine learning technology to be effective, it must undergo a process of education and adjustment in a specific field of specialization. These learning environments should include data sets to be analyzed by machine learning techniques and questions to be answered. For example, machine learning applications used by security analysts will aim to identify anomalous user behavior patterns. On the other hand, machine learning applications for optimizing the operational efficiency of factory robots will be very different from those for security purposes, which is different from machine learning applications for mapping dependencies of micro services based applications.
Developers should therefore be familiar with the field usage of these machine learning applications. That way, you will be able to collect what data, what machine learning algorithms to apply, and what questions to ask. You also need to be able to determine whether a specific area of SaaS or application package is right for a particular project. Considering the fact that a vast amount of training data is needed for machine learning training.
Developers will now be able to use machine learning technology to create intelligent applications that can make recommendations, predict results, and make automated decisions
5. From Dev os to No os
The importance of DevOps, which encourages developers to develop new applications and features and maintains quality and performance levels, is now widely accepted. However, in carrying out the duties of DevOps, 60% of the time resources spent on the existing development work are put into operation, and as a result, the development capability is degraded.
Developers will need to integrate a variety of Continuous Integration and Continuous Delivery (CICD) tools, maintain the level of integration, and continually update the CICD tool chain to keep pace with the development of new technologies. CI (Continuous Integration) among CICD is a subject that everyone cares about, but there are not many developers who care about CD (continuous delivery). In 2018, business developers will rely on cloud services as aids to help them do their "development" work and will require more automation for true CICD.
Dockers provide the user with the ability to enable packaging, portability, and agile development, but CD is a prerequisite to accepting this way of the docker. For example, if you are using a container and you change code to Git, the built-in structure should be a bucket image that contains the new code version. Furthermore, this image should be automatically pushed into the registry of the docker, and containers placed from that image must also be pushed into the development-test environment.
After QA testing, production deployment, coordination, security, and container coordination processes are all under the management of the developer. If you're a business executive who needs developers to innovate faster, you'll have to worry about the new Devox model, which will give you more time for your development work.
2018 will be a year of tensions and anxieties for developers. On the one hand, the expectations and pressure that new technology products and tools such as block chaining, chatbot, serverless technology, and machine learning will mature to the point where they can be put to practical use, but on the other hand, . However, there will be good news in this pressure.
The year 2018 is expected to open up to developers with tight tug-of-war between pressure on hope for new opportunities and better quality work. Let's see how these opposing forces work and how they will affect developers.
1. Commercialization of B2B transactions using block chains.
Companies are increasingly interested in the strengths of security, reliability, and efficiency of block-chain-based transactions. This year, block-chain applications will emerge in various areas such as financial services and manufacturing supply chain. Block chaining is a technology that enables secure, efficient, and always reliable transactions without intermediaries between institutions that can not guarantee mutual reliability.
Suppose a company orders a particular product from a foreign manufacturer. At this time, the ordered goods are transported through a separate transportation company, after passing through customs, and then arriving at the ordering company through the domestic delivery company. Today, e-mail and spreadsheets demonstrate and coordinate all these processes, and the people and processes required to do so are also significant. On the other hand, if the block chain agrees with the fact that the minimum number of persons concerned is 'proof of this transaction', the existing manual process and adjustment process are omitted by updating the contents of the block chain to permanent records in the block chain.
The block-chain cloud service presents a new perspective on scalability, resiliency and security, and ensures a high degree of pre-built integration with enterprise systems. In addition, it will provide developers with an opportunity to move beyond their hyperlinked fabric implementations and focus more on business applications.
2. Chatbot to actually communicate with customers and employees
As time goes on, people will not want to endure the inconvenience of using multiple apps separately to do the same thing. For example, if you need to use different apps for each airline to check in flights and get tickets. One of the best ways to get rid of these inconveniences is to do this using the messaging features most used on smartphones.
Messaging apps are attractive because they are immediate, expressive, and conversational. In addition, you do not need to be trained for use. Thanks to the development of artificial intelligence and natural language processing technology, it is now possible to ask and answer questions to AI bots using voice assistant technologies such as Facebook Messenger, Slack, Wit Chat, Watts App, or Amazon Alexa or Google Home .
Developers can also use intelligent bot production cloud services to understand the intentions of customers and continue conversations and create bots that maintain integration with backend systems in a short period of time. For example, suppose you want to send a photograph of an actor's clothes seen in a movie to a bots in a clothing store that is frequently visited. The bot will use image recognition technology and AI technology to recommend a similar style of clothes to the user's.
The use of these bots is also good for workers. For example, you can use the bot when you ask for a few days to leave, ask for help at the help desk, or order a replacement laptop. Artificial intelligence bots will recommend the right notebook model for the employee and will update the order status constantly. Developers are more likely to experiment with the bots for the first time when creating the first bot. Failure will be even less of a blow when failing to experiment with other employees in the company.
3. Buttonless world: Interfacing with AI
AI is becoming a new standard in UI. With the advent of this new interface, the existing concurrent request-response model of using apps and services will gradually disappear. Of course, realistic smartphones are still 'low-IQ' tools that users have to pick up, run certain apps, and respond to commands. On the other hand, next generation intelligent apps can initiate their own interaction with users via push notifications. And a step further from here, you will experience artificial intelligence apps, bots, and virtual assistants looking for themselves in a variety of contexts and contexts. Here are two examples.
- Apps that automatically analyze user spend report approval patterns and make decisions on spend decisions reach 99% self-approval rating. The app minimizes the risk of errors by requiring manual approval for exceptional reports that require user review.
- Analytic apps are provided to analyze analysts' existing analytics data, business analysts' question history, and other analysts' queries. The larger the amount of data that an organization collects, the better the level of questions the AI will make about that data.
Developers can now identify and experiment with what data is needed for the business app they want to develop, what perspective they will derive from the transactions they make, and the most valid business decisions based on information derived from these predictive AIs. . Built-in AI automates many of the tasks that are currently being done manually, predicting what the user needs and delivering the right functionality at the right time with the appropriate means.
4. The emergence of practical machine learning apps with expertise in specific areas
Machine learning is now moving from the area of abstract data science in the past to the mainstream application development area. The availability of pre-built modules from popular platforms is increasing, and it has been proven that these modules are very useful when analyzing large data sets. The most valuable and important insights in machine learning technology can be gained when contextual information is available. Contextual information refers to the user's past behavior, previous questions, behavior patterns of others, and distinction between normal and irregular activities.
However, in order for machine learning technology to be effective, it must undergo a process of education and adjustment in a specific field of specialization. These learning environments should include data sets to be analyzed by machine learning techniques and questions to be answered. For example, machine learning applications used by security analysts will aim to identify anomalous user behavior patterns. On the other hand, machine learning applications for optimizing the operational efficiency of factory robots will be very different from those for security purposes, which is different from machine learning applications for mapping dependencies of micro services based applications.
Developers should therefore be familiar with the field usage of these machine learning applications. That way, you will be able to collect what data, what machine learning algorithms to apply, and what questions to ask. You also need to be able to determine whether a specific area of SaaS or application package is right for a particular project. Considering the fact that a vast amount of training data is needed for machine learning training.
Developers will now be able to use machine learning technology to create intelligent applications that can make recommendations, predict results, and make automated decisions
5. From Dev os to No os
The importance of DevOps, which encourages developers to develop new applications and features and maintains quality and performance levels, is now widely accepted. However, in carrying out the duties of DevOps, 60% of the time resources spent on the existing development work are put into operation, and as a result, the development capability is degraded.
Developers will need to integrate a variety of Continuous Integration and Continuous Delivery (CICD) tools, maintain the level of integration, and continually update the CICD tool chain to keep pace with the development of new technologies. CI (Continuous Integration) among CICD is a subject that everyone cares about, but there are not many developers who care about CD (continuous delivery). In 2018, business developers will rely on cloud services as aids to help them do their "development" work and will require more automation for true CICD.
Dockers provide the user with the ability to enable packaging, portability, and agile development, but CD is a prerequisite to accepting this way of the docker. For example, if you are using a container and you change code to Git, the built-in structure should be a bucket image that contains the new code version. Furthermore, this image should be automatically pushed into the registry of the docker, and containers placed from that image must also be pushed into the development-test environment.
After QA testing, production deployment, coordination, security, and container coordination processes are all under the management of the developer. If you're a business executive who needs developers to innovate faster, you'll have to worry about the new Devox model, which will give you more time for your development work.
Five Prospects for Software Development Technology in 2018
Reviewed by Haseeb Tec
on
June 21, 2019
Rating:
Reviewed by Haseeb Tec
on
June 21, 2019
Rating:
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