For decades, scientists have tried to use artificial intelligence techniques and algorithms to equip computers with human-like knowledge and behavior. Although more sophisticated than traditional programming, the techniques used have focused primarily on manually growing and improving the system’s knowledge base, which has always been limited. Poor replacements for human competence, i.e., limited topic knowledge, have been shown to exist. AI systems are only as good as their programming (done manually by a human)
The new approach is to build systems that learn from themselves, becoming experts who model and abstract rules from the data fed to them. These systems increase their precision, adjust to the unknowable, and go beyond what was originally programmed in them. Traditional natural language processing (NLP), rule-based reasoning, and knowledge representation techniques are being augmented with machine learning – especially deep learning – to improve AI .Preliminary results are promising: we are seeing new apps emerge with some “intelligence” in a wide variety of domains.
It’s crucial to be ready since the rise of artificial intelligence will have an influence on how software engineers operate. Developers need to understand what these technologies are and how they can apply them, both in the software development lifecycle and in the applications themselves.
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Impact on the Development Cycle
Below we list some of the possibilities of artificial intelligence to software development:
Quickly turn an idea into code
Although there have been advancements in this area due to agile methodologies and business analysis processes, translating a business idea into software code remains a significant challenge. Imagine being able to simply communicate an idea to a machine in plain English and have it translate it into executable code? Although this is still science fiction, it is possible that changes and improvements to an application can be suggested through natural language processing and expert systems. AI will enrich requirement models and test cases with more sophisticated text recognition, resulting in better code generators.
Improve the accuracy of estimates
The estimation of software projects continues to be a complicated activity with low precision, in which it is necessary to involve experts with extensive knowledge of the context. Imagine an estimating software solution that analyses historical data from past company projects to find statistics and correlations, and uses predictive analytics and business rules to provide more accurate estimates of time and effort.
Accelerate defect detection and solutions
Teams put a lot of time and effort into duplicating system faults when they occur in production in order to find and fix them, yet in most cases the team that developed the system is no longer accessible. Through AI, it would be possible to analyze the skills of the person who wrote the original code and locate someone available with a similar profile.
Automate the selection of the next thing to develop and test
An AI could analyze the usage patterns of an application in production and based on that decide which backlog requirement(s) should have higher priority, or be implemented first. The creation of automated test scripts might also be done using this usage behavior analysis.
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Impact on Applications
A new generation of applications that can talk, listen, feel, reason, think and act is coming to our computers, phones and devices. The list of companies building AI-enriched applications is growing rapidly.
Here are some capabilities in next-generation apps made possible by AI:
Natural interaction with humans
Throughout the history of computing we have had to interact with computers through unnatural interfaces: punched cards, keyboards, mouse, capture forms, etc. AI is enabling computers to see and hear their users, in addition to responding by voice in natural language.
Coding domain-specific policies and business rules through traditional programming languages is a complex and effort-intensive activity. Artificial intelligence gives the option of building expert systems focused on a specific domain that can support novices in an activity or help managers in their decision making. Although expert systems are nothing new, so far we will start to see them becoming popular, as well as being enriched through deep learning.
Imitate typical human capabilities
A Brazilian mining company was looking to automate its inventory process and to do so it required being able to correctly identify a large number of train cars. Initially he considered putting RFID tags on each car. However, he realized that all the cars are already visually identified with signs, so using optical character recognition was a better solution.
Software that learns by itself
One of the technologies that will have the most impact on the apps we develop is the combination of deep learning and big data. It will be very interesting to see what applications we will be building next when unsupervised learning is available to everyone.
AI Enables New Kinds of Apps
As a result of artificial intelligence, we will progressively develop hitherto unheard-of application kinds. To create these AI-enabled apps, businesses must expand their creativity and experience. Companies will adopt AI gradually. Forrester Research envisions this process as having 3 stages:
- Make existing apps more “conversational” and fluid. Initially, AI experiments focus on adding “cool” things with a limited scope to enhance user experience and engagement.
- Improve understanding, reasoning and decision making. Through an appropriate combination of data and ontologies enriched with machine learning algorithms, applications will have the ability to reason and deduce information.
- Build apps that are more than just apps. Traditional desktop or web applications will gradually give way to bots and intelligent agents. Developers will no longer focus on programming them but on training them.
Software organizations need to develop capabilities in the field of artificial intelligence. Artificial intelligence will not replace the work of developers, but will enrich it. Creating AI-enriched applications as well as improving development processes through AI will require new skills.
I recommend avoiding the notion of the data scientist as he is super-endowed with business domain knowledge, math, and analytics, programming, and infrastructure management skills. It is more realistic to have people specialized in artificial intelligence (mathematicians) collaborating with people specialized in data engineering (programming and infrastructure management).
The software development process is a candidate to be improved through artificial intelligence. However, for this to happen we require that the processes are defined and implemented. Mature organizations already have this in place and will be the first to reap these benefits, which in turn will enable them to build better software with less effort.
Pryanka is a Digital Marketing Executive at Sparkout Tech Solutions. She designs marketing strategies with the intention of using high-quality content to educate and engage audiences. Her specialties include social media marketing specialist, SEO, and she works closely with B2B and B2C businesses, providing digital marketing strategies that gain social media attention and increase your search engine visibility.