AI, AI, AI. That’s what we’ve been hearing for the past few years. The concept of AI is wide in nature and has its applicability in many sectors. Software engineering is one sector.
Software is developed and implemented by following a certain procedure to maximize the utilization of time and available resources. Software Development Life Cycle (SDLC) is a process for developing software to meet the exact requirements.
How can artificial intelligence help this 7-stage process push the limits and work more efficiently?
AI can do wonders with machine learning models to find business patterns and understand the necessity of developing a software system. Understanding the problem is the first step in every software development.
Developing a software right away without identifying the root cause is like drawing lines on water. Even though the problem is identified by spending a lot of time and effort, not every correlated factor will be used for problem analysis.
AI can resolve this bias to a great extent, not just in the initial stage but in all the 7 stages of software development life cycle.
Like the first stage explained in the above section, there exist 6 other stages to follow in software development life cycle. The 7 stages or phases of SDLC are,
Problem identification is the primary process of every business and takes further steps to resolve it. Ultimately, the need for software development begins from this stage.
Problem identification requires a lot of data; data from business operations to the valuable feedback from different individuals and groups.
The strengths and weaknesses of the business have to be analyzed properly. Alongside, the feedback from customers, employees, stakeholders, etc. are also taken into consideration.
As mentioned earlier, It’s not an easy task and some pieces of data are unused during problem analysis.
AI can help decision-makers in two ways, either using business intelligence (BI) tools or by machine learning. Business data can be accessed from the BI dashboard, where different business metrics are displayed as KPI cards with performance value.
The cards with low performance value cause problems in the business. A pretty simple way of identifying the relative causes, isn’t it?
Or else machine learning, where a machine is fed with all business data in a structured or unstructured way. The machine then analyzes the algorithm and provide insights. The insights are enough for problem identifications!
When an idea is formulated, the next important stage is plotting the documentation based on a plan. Planning is a detailed report that must reflect the ultimate goal of the requirements.
It must be prepared with great care. It is preferred to make a synopsis first and then the big plan.
Here, the cost, resource allocation, timeline, and everything should be mentioned. You can consider this plan as a whitepaper where every requirement is proposed before stakeholders.
Automated document classification based on benchmarks like cost, resources, etc., can be achieved using machine learning.
After feeding the machine with data from the first stage, the desired target is set as input. The system then calculates the rest and predicts the value for each benchmark.
For resource allocation, employee behavior analysis is considered and then allocated as per their abilities. The timeline is set based on the skills of each resource and their past abilities of goal completions with the organization.
After the plan gets approved by the concerned team, a software prototype or concept is built. It is designed accordingly using the patterns of software architecture and development.
The concept model is considered as the first step in software development. The software architecture is built based on the software flow prepared during the documentation stage.
Coding begins from this stage but with the least functionalities. The aim is to test the feasibility of the programming language used for software development.
In short, a demo version is provided to decide if it’s worth proceeding with the development of a fully functional system.
Machines can check the output of the concept models by examining the pre-determined benchmarks. A value can be provided for each point like whether the coding language choice is reliable or not, the drawbacks, and much more.
Basically, predictive analytics is the process used here. The historic data (the goals) are compared with the results of the software prototype and the output is predicted.
Further processing to the next stage can thus be backed with proper data and the decision will be made by the machine. As the decision-maker, we have to use our intellectual ability to accept or reject the decision.
The coding structure, with the entire functionalities as per the requirements, is developed in this stage. An agile process is followed after the acceptance of the concept model. Maximizing the output by managing the timeline is considered very vital during this period.
The right programming languages are used to provide a stable system with less or no error. However, errors happen and the best practice to follow is to make them as low as possible.
AI eases the efforts of developers by providing a programming platform that possesses machine learning. These platforms provide auto-generated code suggestions while preparing the codes. Just like the way Google provides search suggestions based on the query or input.
Redundancy is well maintained in each task of development. The facilitation of such assistance is provided by Kite, Codota, etc. Following this method, companies can make their developers proactive as well as increase productivity.
Code testing is the next task followed after the development stage, where the entire coding structure is checked and optimized. In this stage, apart from the code testing, certain other aspects are also examined.
Here, the quality assurance of software, the flow of coding, the scalability for the future customizations, defects, etc. are checked and compared with the requirement chart or documentation.
Software testers, either manually or using software, ensure meeting of the software goals. Developers have to fix all the errors reported by the testing team and make the changes to ensure high-performance.
Historic analysis and the previous projects based on different customer requirements are considered as data in this process. The testing team and developers get an option to feed the errors or bugs to the machine.
A pattern is then formed by the machine and it recognizes these errors when occurred again. This creates less workload as the machine rectifies it automatically or through auto-suggestions.
Testers can also flag errors during this stage. The other concerns that will be answered during this stage are the latest security compliance, user interaction, etc. A smart way of debugging the errors and providing the right system is by following the software hierarchy.
After diagnosis, the system finally reaches a state of equilibrium, i.e., a stable software platform. What happens next? Of course, deploying the system! The software development life cycle meets the final software release state after completing the pre-stages successfully.
The bugs that still appear in the software are rectified as soon as possible. The feedback from the customers is collected and implemented in this stage of the software development life cycle process.
As we connect AI through the machine learning process, it is pretty much clear that the deployment status can also be processed with the help of machines. New feedback from the customers are collected and analyzed to generate new suggestions for developers.
These insights help developers in the next stage where changes are implemented for the future. The system itself can optimize this code as per the feedback but it’s recommended to have human intervention.
Together we call it hybrid intelligence, the ability of humans to behave as per emotions and analyzing power of machines hand-in-hand.
The final stage of software development, where the software is reliable and scalable to accept new changes as per the customer requirements. Software maintenance is also known as the ‘support stage’ of a software development cycle.
Make sure the demand of the software stays alive or else it reaches an end-of-life status where customers no longer need the software.
Also, if the system reaches its higher applicability state where no further update isn’t required, then it is said to have achieved ‘saturation’ stage.
Machines require data, the present technologies, market plagues, and rises, stocks, basically, everything that can be accessed from the internet.
Nowadays, even the unstructured forms of data are analyzed using Natural Language Processing (NLP). Analyzing the present data with new requirements, the machine will suggest new innovations that can be integrated with the software.
If manually checked, we will say, these are the ‘possible’ methods where machines say, ‘these are the best methods’. See the difference here, the insights or predictions made by the machine are purely based on data.
However, as human beings, we have to go for experiments to find expected results. Hence the ‘possible’ scenario arises. Smart decisions can be made quickly with such a useful machine learning process.
Thus, AI provides massive assistance in each stage of software development. Machine learning helps to ease this process and increase productivity to a great extent.
Machines have such ability in providing useful insights, however, it’s necessary to include human intervention.
Hybrid intelligence and NLP are significant in this scenario. Yes, it’s presently in the first phase of development.
So it takes time to inhibit the abilities of AI in the above 7-stage software development life cycle process. It is recommended to start applying them now so that you will understand the ability over time and get used to the new phase of software 2.0 or the early stage of AI’s adulthood.
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