How To Improve Software Product Development With Machine Learning

The software development industry has made huge strides since the launch of the first iPhone, and it continues to do so at an ever-increasing pace. 

Today, it is Machine Learning (ML) and AI, which are the latest cutting-edge technologies that promise to radically change the world we live in. They are set to change not just industry but our daily lives too. Read on, as I explain how.

The ever-growing demand for software products fosters innovation in the software development industry

A recent Statista report states that the global market for software products was $532.49 billion in 2020. The report projects this market to reach a staggering $772.45 billion by 2025.

This growth in demand for software products isn’t a new phenomenon in the software development industry. Indeed, the software industry has always had to quickly and radically innovate to keep pace with demand growth. A few examples are as follows:

• Creating ever-more-powerful processors;
• Reducing the cost of data storage;
• Creating modern programming languages like C, C++, Java, Python, etc.;
• The open-source movement;
• Creating new generations of SQL and NoSQL databases;
• Cloud computing.

One key trend of note is that software developers have always had to innovate to automate or reduce mundane work, this even includes help with writing code and code testing, etc. This allows them to focus on higher-value workstreams.

Machine Learning, a subset of Artificial Intelligence (AI) is increasingly proving its mettle in enabling software engineers to create more and more value. Let’s see how.  

1. Using ML and AI to manage software development projects better

ML and AI can help project managers (PMs) to manage complex software development projects.

What’s the problem at hand? The growing complexity of managing software development projects

There is a growing complexity with managing software product development projects. The rise in complexity is due to the following reasons:

• New delivery models: The advent of cloud computing has opened new delivery models like SaaS (Software-as-a-Service) for software product vendors. New delivery models bring new complexities too.

• Markets that were previously untapped: Software product vendors now sell to customers in markets all over the world.

• Novelty: Cutting-edge technologies like IoT, AI/ML, and blockchain help software product vendors to add entirely new capabilities into their products. They often offer FOAK (“first of a kind”) products, and such projects are complex.

• Other factors: There’s a growing focus on security, scalability, maintainability, etc. PMs managing projects in an enterprise need to navigate these dynamics effectively. Naturally, these factors increase the complexity of the project, particularly for first-time projects and inexperienced project managers.

How ML and AI can help to manage software projects

There’s plenty of data created when undertaking software product development projects. This can be used to provide valuable insights to PMs. However, PMs can’t possibly derive these insights manually from huge amounts of data sets. This would be too time and energy-consuming.

ML algorithms, on the other hand, can swiftly derive insights from massive data sets. Project management tools incorporating ML and AI can help PMs quickly gain valuable project insights. Such tools can offer the following:

• Insights about the effectiveness of particular software development teams;
• Forecasts about the KPIs around cost, quality, schedule, etc.;
• Risk analysis and forecasts;
• Recommendations about correct actions and risk mitigation.

An example of using ML and AI to manage software development projects

Easy Projects is an excellent project management software application. It uses ML and AI to offer useful features to PMs. Easy Projects uses ML algorithms to analyze various aspects of project development  including:

• Performance track records of project teams;
• The history of projects/phases/tasks completed.
• Based on this analysis, Easy Projects predicts many aspects, e.g.:
• The likely completion date of a project;
• The confidence level in the prediction.

PMs can use these predictions to fine-tune their risk mitigation plan. They can take preventive and corrective actions early to keep projects on track.

2. Reducing the maintenance costs for regression and continuous testing environments with ML and AI

ML and AI can reduce the effort and costs to maintain the regression and continuous testing environments.

The problem: Maintaining regression and continuous testing environments is costly

Test automation helps testers. However, there’s a hidden cost. Maintaining test environments takes time, and it’s costly.

Consider a scenario where the development team has changed the app. You can’t use the old test environment anymore as it is no longer relevant. You need to update the test environment, otherwise, the test coverage gap will increase.

Testers need to update the test environment and scripts. This requires manual effort.

Changing test automation scripts requires coding, therefore, you need testers with the relevant skills. Updating regression and continuous testing environments repeatedly increases the test environment maintenance costs.

What ML and AI can provide

Testers do the following to maintain the regression and continuous test environments:

• They review code changes done by the development team.
• Testers analyze new features and paths in the code.
• They determine whether the current test scripts and environment can test these changes.
• They manually modify the test environment and test scripts.

ML and AI-powered solutions can do all of these quickly. They use ML algorithms to analyze the code and detect new features. 

These ML-based solutions can analyze the new paths in the code. They can therefore determine whether the new code introduces new features of bugs. ML test automation solutions can identify whether the test environment is adequate to test them. Finally, these solutions can modify the test environment and scripts.

An example of ML/AI-powered automated web testing tools

Perfecto Scriptless is an ML/AI-powered automated web testing tool. It was formerly known as TestCraft. Perfecto Scriptless improves test automation for web apps.

It caters to functional UI testing of web apps. Perfecto Scriptless expedites the creation and execution of test scripts, and it makes maintenance of the test environment easier.

This ML and AI-powered solution reduces the coding effort on the part of testers. Testers don’t need to spend a lot of time on test environment maintenance.

Perfecto Scriptless addresses the problem of flaky tests by utilizing the power of ML and AI. It fixes the majority of broken tests without manual intervention from testers.   

3. Reducing coding efforts with the help of ML and AI

You can reduce the coding effort on the part of your software development team with the help of ML and AI.

The problem at hand: Repetitive coding tasks

Programmers have always wanted to automate repetitive coding tasks. Over time, they came up with many ways to reuse code. COBOL copybooks were an example of this.

Libraries and frameworks in more modern programming languages helped developers to reduce time spent coding certain solutions. Software engineers also created code generations tools.

However, these tools have certain limitations. Programmers need to manually review and incorporate them. Even traditional code generation tools use rules-based automation.

How ML and AI can help

ML and AI can help programmers to go beyond rules-based automation. With the advent of the open-source movement, we have open repositories like GitHub. Many projects have made their code available to others via GitHub. These projects cater to a wide range of industries, functionalities, and technologies.

Repositories like GitHub are massive treasure troves of code. ML and AI-powered tools can “study” these code repositories. They can study various codebases catering to different functionalities.

Subsequently, they can generate codebases for specific functionalities. Developers don’t need to write the entire code for a given functionality since an ML-powered tool has done that already. Software engineers can review and provide that “expert touch”. This has made such ML systems a huge leap forward!

An example of ML/AI-enabled tools to reduce coding efforts

“Autocomplete from Stack Overflow” is an ML-powered tool used to create code. It was created by Emil Schutte, an American software engineer. At the time of writing, this tool can generate JavaScript code only.

This ML-powered tool has “learned” a vast amount of coding from Stack Overflow. It can identify the intended functionality from the partly-written code in a software project.

“Autocomplete from Stack Overflow” can then write the complete code for the target functionality. It uses ML-powered “intelligent automation” for this.

4. Better code review with ML and AI

ML and AI can improve the code review process in your software product development project.

The challenge: The huge scope of code review

Code review is a vast workstream in any software development project. For complex projects, the scope of code review can be vast.

You need highly experienced reviewers to review thousands of lines of code. What if you use niche technologies like blockchain? You might find it hard to onboard such experienced reviewers.

Expert code reviewers use their experience as well as getting help from established coding guidelines and code review checklists. However, this manual review takes plenty of effort. Reviewers might also miss bugs due to the vast amount of code to be reviewed.

To expedite this process, you can use code review tools. They can find many common errors, and, as such, they can help reviewers. Reviewers can focus on complex paths in the code. This can improve their chances of finding bugs.

However, code review tools have limitations. They produce far too many “false negatives” and “false positives”. This reduces the level of confidence in the overall code review, and reviewers may well decide that they need to manually review the entire code anyway.

How AI and ML can help with code review

ML-powered code review tools have “rules”. They review code using them, however, that’s not unique. Experienced code reviewers and traditional code review tools have “rules” too.

What’s unique with ML-powered code review tools is that their rules keep growing. That’s because ML-powered tools are continuously “learning” when they review new codebases.  

With the help of this “learning”, ML-powered code review tools can understand the intent of codebases. You can “train” these tools by feeding them codebases of millions of projects available in repositories like GitHub. 

ML-based code review tools can find more bugs than conventional code review software products, and even human developers, due to these advantages. These tools can also find these bugs faster due to “intelligent automation”. They can help expert reviewers more than the traditional code review tools.

An example of ML/AI-enabled code review tools

DeepCode is an ML/AI-powered code review tool. A group of ML and AI researchers founded this company in Switzerland. DeepCode is an interpretable ML-based semantic code analysis tool.

It can scan code 10-50 times faster than traditional code review tools. At the time of writing, DeepCode supports Java, JavaScript, Python, TypeScript, C, and C++.

The founders claim that DeepCode has far more rules than other code review tools. They also stressed that the number of rules is growing steadily. Independent reviewers found that DeepCode produces qualitatively better results than traditional code review tools. 

Snyk, a leading provider of cloud-native application security solutions acquired DeepCode in 2020. It found DeepCode far superior to traditional code review tools.

5. Automate visual testing with ML and AI

You can transform visual validation testing in your organization with ML and AI.

The problem: Visual testing isn’t easy

Visual validation testing typically involves many manual steps. 

Testers need to set up visual processing settings. They need to set up various parameters like percentages. Furthermore, testers need to configure many more aspects for visual testing. Your testing team needs to repeat these steps for different platforms.

Can ML and AI help with visual testing?

What if you are able to use a testing tool that doesn’t require these manual configurations? ML and AI-powered tools can achieve this thanks to their learning algorithms.

These ML algorithms “learn” from vast data sets. They are adaptive. Testers don’t need to specify visual elements for testing, and ML testing tools can identify bugs as mentioned above.

An example of using ML and AI in visual testing

Applitools is an automated visual testing platform that uses visual AI. Testers don’t need to configure visual testing-related parameters since Applitools has adaptive algorithms that do it automatically. You can run visual UI testing without specifying the required elements.

Applitools offers the following advantages:

• Better test stability with the help of visual AI;
• Increased test coverage;
• Reduced test environment maintenance;
• Faster cross-browser and cross-device functional and visual testing;
• Integration with popular test frameworks, source control tools, build servers, and collaboration tools.

6. Low-code and no-code testing with ML and AI

ML and AI open avenues for low-code and no-code testing in your organization.

The challenge: Testers need to code a lot

You might face the following testing-related challenges often in your organization:

• Creating test cases takes time.
• Test execution takes time.
• You need to manually modify or add test cases every time your developers modify the application.
• Creating test data takes time.
• Testers in your team need to spend considerable time and effort fixing broken tests.
• Your test environment doesn’t scale well.

Testers need to devote significant effort to address these, and they need considerable coding skills.

How ML and AI can reduce the coding efforts of testers

AI and its various subsets like ML, NLP (Natural Language Processing), DL (Deep Learning) can address these challenges. AI-powered testing tools can expedite test case creation, and they can expedite test execution.

ML and AI-based testing tools reduce the maintenance effort on the part of testers. These tools can identify changes in the application and create test cases. They offer a low-code/no-code solution to testers, and they make the test environment scalable.

An example of a low-code testing solution powered by ML/AI

The Sauce Labs low-code testing platform is an example of this. It uses ML, AI, DL, and NLP. The platform offers the following advantages:

• Faster test case creation;
• Expedited test execution;
• Automated detection of changes to applications;
• Automated creation of test cases to cater to the above-mentioned changes;
• Reduced test maintenance efforts;
• Fixing broken tests with DL.

Testers don’t need to perfect their coding skills, therefore, they can focus on testing.

 7. Reducing flaky tests and test maintenance with ML and AI

ML and AI can help you provide test stability. They can reduce flaky tests and test maintenance.

The limitations of traditional test automation solutions in providing test stability

Traditional test automation solutions can automate several aspects of testing. However, they have limitations when it comes to testing stability.

Testers need to spend plenty of time to eliminate flaky tests. They need coding skills for this, furthermore, they need them for test maintenance.

Testers need to manually create test cases when developers change the application. Developers need to intervene when you use traditional test automation solutions.

Can ML and AI help to improve test automation?

ML and AI can do the following:

• Create tests quickly;
• Analyze large-scale testing-related data to find important attributes in the test environment;
• Utilize these attributes to provide test stability;
• Automatically scan test execution cycles;
• Compare prior and current test execution cycles;
• Identify repeated sequences and duplicates;
• Reuse code to reduce test maintenance.

These capabilities reduce flaky tests and test automation. They reduce the dependency on developers, and testers can focus on the important work.

An example of ML and AI reducing flaky tests

Testim is an ML and AI-powered end-to-end test automation solution. While several AI-enabled test automation solutions exist, Testim focuses on reducing flaky tests. It also reduces test maintenance. It does the following to achieve this:

• Its AI capabilities recognize repeated sequences and suggest reusable elements.
• The “smart locators” of Testim are AI-based. They compare prior tests to current ones.
• The “smart locators” detect changes in the app, and they “improve” themselves.
• Testim scans all tests automatically. It replaces repeatable and duplicate sequences with reusable groups.

It offers the following other advantages:

• Testim helps to create tests quickly.
• It provides a visual editor for customization, and it offers the “Root Cause Analysis” capability.
• Testim helps to scale tests.
• It integrates with popular tools and provides robust analytics. 

8. Improved software quality governance with ML and AI

The term “software quality governance” refers to the automated identification, management, and control of software quality risks. These risks can arise due to any changes in the software.

They can manifest at any stage of the delivery pipeline. ML and AI can help you to improve software quality governance.

The challenges of implementing software quality governance

Software product development teams face the following challenges in implementing a robust software quality governance:

• There are frequent releases. Developers, testers, and DevOps engineers remain busy managing the CI (continuous integration)/CD (continuous delivery) practices. They might not get time to test the entire application sufficiently.
• Testers can’t easily find out whether the tests they create cover all branches/paths of the code.
• They can’t get results if a test covered a particular change to an app in the last build.
• Your team might have plenty of data in the CI/CD environment, however, the team doesn’t get insights.

How ML and AI can help

ML algorithms can analyze your code and test environments. They can find out if tests cover all the branches and paths in the code.

ML-powered tools can find out if changes in the last build were tested. Due to this, ML-based tools can highlight software quality risks. These tools can provide insights from the CI/CD environment.

An example of implementing software quality governance with the help of ML and AI

SeaLights is an ML-powered software quality governance platform. It collects telemetry data from all stages of the SDLC.

Thanks to this data, SeaLights can identify software quality risks in the delivery pipeline. It can analyze and communicate these risks.

Testers are left to focus their testing efforts on important branches/paths of the code. SeaLights uses AI algorithms to score software quality risks.  

Conclusion

In this article, we reviewed how ML and AI can help to transform software product development. ML can make a huge difference in areas like project management, coding, code review, and testing.

We reviewed a few examples of ML helping programmers and PMs. ML is an evolving technology, therefore, watch this space to find out more about developments as they progress.