- The overall AI market includes an extensive range of applications, such as robotic process automation, natural language processing, and machine learning.
- Machine learning is expected to change the fundamentals of software development.
- By 2040, AI technologies, such as natural language processing and machine learning, will become highly advanced.
The influence of Artificial Intelligence (AI) has spread across a wide range of industries, and the software development field is no exception. According to industry experts, AI and Machine Learning (ML) technologies will impact every aspect of the software development lifecycle. From requirement gathering to software implementation and troubleshooting, AI and ML benefit programming in multiple ways.
Tractica reports that the global AI software market is projected to grow significantly, with revenues increasing from approximately $9.5 billion in 2018 to $118.6 billion by 2025. The overall AI market includes an extensive range of applications, such as robotic process automation, natural language processing, and machine learning. The impact isn’t limited to industries alone — consumers interact with AI on a daily basis while using their making use of some of the most trivial mobile services on their smartphones.
Challenges in the software development industry
Traditionally, software development requires programmers to specify the requirements in advance and then hand-engineer all features of your technology. Encoding many tasks and decisions can be too challenging to teach computers in a rigid, rule-based manner. Even an activity as simple as identifying objects in an image is difficult for traditional software development. No software team can enumerate all rules that would reliably identify objects in the image.
This is when AI techniques come in, especially ML and deep learning. With AI, engineers do not specify rules to the computer regarding how to make decisions and take necessary actions. Instead, the machine prepares domain-specific data that is used by learning algorithms that are continuously improved.
The role of machine learning in software development
Machine learning is expected to change the fundamentals of software development. According to Google’s Jeff Dean, 500 lines of TensorFlow code have replaced 500,000 coding lines in Google Translate. This has led to a thousand-fold reduction in programming effort as well as the volume of code that requires maintenance.
A neural network has been deployed and trained to translate, which replaces half a million lines of statistical code. The neural network can be retrained on any new data to upgrade the translator. Though training a neural network can be slightly difficult, its ability to solve the problem of managing and debugging a gigantic codebase cannot be overlooked.
Neural networks can combine existing modules to create new programs. The network is trained using execution traces from other programs so that a single network can perform multiple, different tasks, each of which otherwise, require a separate computer program.
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How machine learning will impact data management and infrastructure
Machine learning can perform better than traditional database indexes. ML can learn to predict data storage, even if the data is yet to exist. This AI technology is considerably faster and consumes significantly less memory.
Undoubtedly, current ML-based tools do not cover multidimensional indexes. Retraining requires longer amounts of time than rebuilding traditional database indexes. However, researchers and data scientists are working on multidimensional learned indexes, re-training performance, query optimization, and other related issues.
Machine learning is also influencing other areas of data infrastructure. For instance, data engineers are already using machine learning for Hadoop management, which enables quicker problem-solving. Data researchers are successfully using machine learning to improve the performance of databases. ML helps simplify the management of configuration settings that impact behavior.
These ML benefits will impact the industry further by requiring data engineers and database administrators learn and master the technology as a skill.
AI will improve the software development process
Even if there is an evident hype around AI for software development, the technique is still in its infancy, and it will be years until it can be used on a larger scale. Also, there are some aspects that are even better managed by conventional software. Here are some areas where AI and machine learning can make a difference.
- Creating MVPs fast
Traditional computer coding involves months of requirement gathering, planning, and preparation before beginning a project. Collecting funds with prototyping is another challenge which requires several important resources.
With ML, this cycle is shortened to a few lines of code, or even to a simple drag and drop task. An example would be creating a chatbot either by using predefined natural language libraries or a user-friendly, no-code platform. Such a task would have taken a significant amount of time with a standard computer programming language such as C and C++.
- AI and project management
Data such as delivery times, delays, common pitfalls, and others can be used to train an automated AI-based system so that they produce accurate estimates. In this scenario, deep learning is the best choice.
Here, details of past projects, such as bugs, estimated and actual values, user reviews, etc., are needed. This will provide estimates for the delivery schedule and help outline obligations in the initial contract. As the program spends more time measuring the team’s performance and challenges, it learns about the habits of all team members, and can provide personalized work schedules.
AI has the unique ability to monitor patterns, which make AI-based systems work as capable assistants to project managers. According to some studies, project managers spend over half of their time on administrative tasks. AI bots can step up and handle less intensive tasks, allowing project managers to focus on other issues.
- Automatic debugging
Pattern detection deeply identifies and classifies different error types in the software. The AI-powered deep learning algorithm can flag recognizable errors, fasten the debugging process, and even learn how to fix each error. After required training, the system could automatically correct many mistakes in a manner similar to the auto-correct feature on smartphones.
How AI will change software testing
AI-powered unit testing tools help developers reduce flay test cases and maintain unit tests easily. AI will speed up time-consuming activities for all developers, such as writing all programs manually. The technology will also help analyze large amounts of data sets faster than the existing speed.
A Forrester Research report suggests that the bulk of the interest in using AI in the software development industry lies in bug detection tools and automated testing. AI can sort through log files, saving time while improving the program accuracy tremendously. This will provide quality assurance (QA) engineers a complete view of the required changes and upgrades.
QA processes involve several errors, which many times remain unnoticed even after careful debugging. AI answers questions such as how, when, and where in a matter of seconds. Testers use this information to decide whether they have to make coding changes to prevent and eliminate programming errors, or apply some other techniques. Accordingly, the AI in this scenario can conduct ongoing error analysis.
Unnoticed bugs are a common problem that bear extremely negative consequences. Not paying enough attention to data management often leads to many ignored bugs. Consequently, this then leads to unsatisfied customer, which negatively affects the brand’s reputation. In this scenario, the machine learning approach offers highly reliable outcomes, and the time required to carry out software testing is reduced.
The black box effect
Although AI-powered algorithms deliver excellent predictions and automation, they have a definite downside. The learning method of these algorithms is opaque to the outside coders, testers, and debuggers. For this, they have to feed it with new data sets and analyze outputs. This can result in very biased and hazardous results.
In addition to these, AI can generate simulations and provide the best features for the success of software products developed based on use rates, and by analyzing the customers’ voice as retrieved from product reviews.
Final words — AI vs the career programmer
Evans Data Corp conducted a survey of 550 software developers, which revealed that these professionals are concerns about programmers being replaced by artificial intelligence.
Researchers at the U.S. Department of Energy’s Oak Ridge National Laboratory report that by 2040, AI technologies, such as natural language processing and machine learning, will become highly advanced. In fact, AI will be capable of writing highly improved software codes at a faster rate than the best human programmers.
However, it’s highly unlikely that AI will replace human programmers. It will take a significant amount of time before AI will become capable of creating real, production-worthy, and quality codes than span more than a few coding lines. Instead, AI will help developers in identifying and evaluating their options. Programmers will be able to decide how to optimize activities that AI cannot understand, while AI assists programmers in turn, creating a symbiotic relationship of some sort.
AI will also help developers with strategic decision making. Developers and industry experts have to spend a significant amount of time deciding what products and features are valuable and which are not. An AI system trained on past software development projects and business factors can evaluate the performance of existing applications. It can also help business leaders and software programming teams identify keys to maximizing impact and minimize risk.
Overall, the impact of AI on the software development industry has been positive so far and will continue to be, enhancing the field’s lifecycle to a great extent.