Machine Learning In Future Software Development

With the Internet being an inseparable part of our lives, businesses of all kinds are using it to reach a greater number of customers. This has made software development a strategic activity for businesses, however, developing software can be hard.

Sub-optimal practices combine with inherent complexities to make software development a challenging proposition, and businesses are exploring solutions to simplify this. Machine Learning (ML) is one such technology solution.

Are you a business leader trying to improve software development in your organization? You ought to read this guide on how machine learning is changing software development.

Contents

Software development: Higher spending, and sub-optimal value
The factors behind the modest success in software development
What is machine learning (ML)?
How ML works
Machine learning use cases
The global market for machine learning
How ML can transform software development
Planning to transform software development in your organization with the help of machine learning?

Software development: Higher spending, and sub-optimal value

The market for software development is growing significantly. This market was worth $429.98 billion globally in 2017, and it‘s poised to reach $507.23 billion in 2021, according to a Statista report.

Does this higher spending translate to value? Researchers are showing that it doesn‘t, as only 42% of Agile projects and 26% of Waterfall projects were successful. You can read about this research in “Agile projects are more successful than traditional projects”.

The factors behind the modest success in software development

Multiple factors impact the success rate of software development projects adversely, e.g.:

  • Software development teams typically face a large backlog of projects to complete, however, they have limited capacity. Read more about this challenge in “Interesting facts about software development: statistics”.
  • There is a level of abstraction in software, which can make it hard to create requirements clearly, moreover, requirement documents are often not well-written. To compound matters, requirements keep changing, as you can read in “Software engineering | challenges in eliciting requirements”.
  • IT infrastructure issues can adversely impact a software development project.
  • Businesses often find it hard to hire and retain the right talent.
  • Developers often don‘t follow the relevant guidelines and best practices, and this exposes software applications to cybersecurity risks.
  • Defining the quality standards can be hard, and managing a test environment that helps you to meet such standards can be harder!
  • IT organizations often find it hard to integrate different systems and technologies. You can read more about it in “Remarkably useful stats and trends on software development | GoodFirms research”.

What is machine learning (ML)?

Before understanding how machine learning (ML) can help software development, let‘s understand what ML is. ML is an advanced technology that uses Artificial Intelligence (AI) to create computer software that can “learn”, and the system improves its performance over time.

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There isn‘t any explicit programming done for the aforementioned software to learn and improve, instead, there are ML algorithms that “train” the software. The key to this learning is a massive amount of data.

ML algorithms study and analyze this data, and they observe examples, experiences, instructions, etc. Based on analyzing a large data set, these algorithms then identify patterns. The software then goes on to make better decisions and predictions, as explained in “What is machine learning? A definition”.

How ML works

The foundation of ML is computer algorithms, and there are various kinds of them. The key ML algorithm types and their modes of working are as follows:

1. Supervised learning

These algorithms train a computer system using known input and output data. Supervised learning algorithms take a set of known input data, moreover, the set also contains known responses to questions.

These algorithms use techniques like classification and regression, and their utility is in predictive modeling. Linear regression and neural networks are a few examples of such algorithms. You can read more about them in “What is machine learning?”.

2. Unsupervised learning

These algorithms deal with input data sets where the responses to questions are unknown. Since there are no labeled responses in the data, these algorithms train the computer system on finding hidden patterns and structures in the data.

Unsupervised learning algorithms are utilized to detect unusual transactions in banks, and “k-means clustering” is an example of unsupervised algorithms. Descriptive modeling is a use case that uses these algorithms.

3. Semi-supervised learning

In the real-world scenario, most data sets contain both labeled and unlabeled data, therefore, ML needs to use a combination of supervised and unsupervised learning. Semi-supervised learning algorithms do exactly that. You can read more about them in “Machine learning types and algorithms”.

4. Reinforced learning

These algorithms are different from the above-mentioned types since reinforced learning algorithms focus on making the computer system learn by trial and error. There is a feedback loop at play here, and the computer system learns from past experiences.

Q-Learning is an example of a reinforced learning algorithm. Computers playing board games like chess use reinforced learning algorithms, moreover, self-driving cars also use such algorithms.

Machine learning use cases

ML is a promising technology with many use cases, e.g.:

  • Businesses can use ML for Intelligent Process Automation (IPA), which combines AI and automation. IPA can automate simple tasks like routine data entry, moreover, it can also automate more complex tasks like insurance risk assessment. ML adds significantly to rule-based automation, therefore, businesses can save cost.
  • The sales function in any business generates plenty of data, therefore, ML algorithms can learn from this and optimize sales and marketing for businesses. Predictive lead scoring, intelligent ad placements, etc. are a few examples of ML at work to optimize sales and marketing.
  • Virtual digital assistants and chatbots can learn from a massive amount of customer interaction data, and provide intelligent solutions to many customer queries. This frees up the customer support team to focus on more complex customer queries.
  • ML can help cybersecurity efforts since predictive analytics can help you to detect threats early. Behavioral analytics can help in detecting suspicious behaviors. ML can help businesses to analyze a large number of data logs from mobile and IoT devices, subsequently, behavioral analytics can profile potential cyber-attackers.
  • Real-time language translation, chatbots, image intelligence, etc. help enterprises to improve collaboration in their workforce.

You can read “Top 5 use cases for machine learning in the enterprise” to learn more about ML use cases.

The global market for machine learning

As you can see, ML has very important use cases, therefore, the technology has a growing market. The growth potential of ML is clear from the following market research reports:

  • The global market for ML was $1.41 billion in 2017, and it will likely reach $8.81 billion in 2022, according to a MarketsandMarkets report. This report projects a CAGR of 44.1%.
  • Zion Market Research projects the global ML market to grow from $1.58 billion in 2017 to $20.83 billion in 2024, and they project a CAGR of 44.06% in this period.
  • PR Newswire quotes a Research on Global Markets report, which estimates that the global market for ML will grow to $19.40 billion by 2023. This report states that the CAGR for this market between 2018 and 2023 will be 48.3%.

Some of the key players in this market are Microsoft, IBM, SAP, SAS Institute Inc., Google, AWS, Baidu, BigML Inc., Fair Isaac Corporation (FICO), HP Enterprise Development LP, Intel, Oracle, TIBCO, Dell, and Teradata.

How ML can transform software development

ML can bring several positive changes to software development, and I will now explain them.

1. Detect deviations from coding guidelines

Well-written software must meet its functional and non-functional requirements, moreover, it should also follow relevant coding guidelines. Programmers that follow coding guidelines consistently write code that avoids unnecessary complexities, furthermore, such code is easy to understand. You can read more about the importance of coding guidelines in “Importance of code quality and coding standard in web application, mobile app and software development”.

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How do you ensure that your team follows coding standards? Well, code review is the only way, however, that‘s an expensive affair! You need to ensure that reviewers focus on what matters, therefore, you need tools that can find common deviations from coding standards.

ML can help here since ML-powered tools can find such common deviations. This can have a significant positive impact on your software development projects since coding standard deviations give rise to many application security risks, as the Open Web Application Security Project (OWASP) Top 10 Application Security Risks – 2017 report highlights.

2. Obtain insights from code

If you are a senior leader in an enterprise IT division, you know how complex it can get! Various factors create a complex environment in most enterprise IT departments, e.g.:

  • There are far too many projects with conflicting priorities.
  • Different vendors have sold products that address the same functionalities, and now you have too many silos!
  • Different business leaders set up their communication channels with the IT team and sponsor their projects, which stretches the thin IT capacity.
  • The IT department is tasked with maintaining the existing systems, moreover, the same team is often tasked with new development. This creates conflicting priorities.
  • A typical enterprise IT division has many external IT consultants, and it‘s hard to ensure that their work aligns with the enterprise strategy.
  • You find it hard to get a reliable view of the capabilities and skills of your IT team.
  • Different IT managers prefer different frameworks and tools, and now you have a huge portfolio of them, without knowing whether all of them are useful!

Read “Here‘s why enterprise IT is so complex” to learn more about the complexities in an enterprise IT organization.

You need to simplify this, and that will likely be an important transformation. However, you need insights even to plan such a project and ML can help here.

You can run an ML-powered tool to study your code on repositories like GitHub, and gain actionable insights. An example of such an ML-powered tool is source{d}. It can provide several key insights, e.g.:

  • What is the extent of legacy code in your IT portfolio?
  • Do you have code that isn‘t maintained?
  • How many apps do you have that aren‘t adapted to the cloud?
  • What % of your apps isn‘t containerized?
  • What slows down your development process?
  • How often do you reuse code in your organization?
  • Who are your top-performing programmers?
  • How effectively does your team collaborate?
  • What key skills do you lack in your team?

3. Simplify software development project management with ML

Managing a software development project can be complex in an enterprise IT context. Software development project managers (PMs) need to contend with complexities in several tasks, e.g.:

  • Schedule and cost estimation;
  • Tracking project status and costs;
  • Managing quality;
  • Risk management;
  • Human resource management.

You can read more about this complexity in “Software engineering | software project management complexities”.

ML-powered PM tools can help PMs to navigate this complexity. Easy Projects is a good example of a provider offering such capabilities, and its solution offers the following features:

  • Easy Projects uses ML and AI for project forecasting, and its ML algorithms can help PMs to forecast when the project will be completed.
  • These algorithms factor in several variables like the project team composition, the past performance of the team members, the rate at which the team completes its tasks, etc.

You can read about this solution in “Machine learning project forecasting”.

ML-powered tools can use organizational information repositories and external data to help the PM to identify risks. You can read more about this in “Traditional vs machine learning for software development paradigms”.

PMs can use ML-powered tools to create network diagrams, work breakdown structures (WBSs), etc. Such tools can expedite the review of key project documentation, and they can help with project status tracking. We at DevTeam.Space use data-driven processes including AI and ML-powered real-time dashboards.

4. Make coding, code review, and testing easier with ML

As a senior leader in an enterprise IT division, you know that there are plenty of manual, repetitive tasks in coding, code review, and testing. Well, ML is now bringing a whole new wave of automation that is well beyond the rule-based automation that you have seen earlier. Let‘s review a few examples:

Stack Overflow Autocomplete

Emil Schutte, a San Francisco, California, USA-based software development expert has created Stack Overflow Autocomplete, which can reduce coding effort. This ML-powered tool currently works for JavaScript development only.

This tool is far more than simple rule-based automation since it can understand the functionalities offered by code in Stack Overflow. It factors in the intended functionality and creates new code from what it has “learned” from Stack Overflow.

DeepCode

We all know that code review involves plenty of manual effort. If a tool can diligently find serious coding errors, then it can help experienced reviewers. DeepCode goes beyond traditional code review tools since it “learns” from source code repositories to find critical bugs in the code.

DeepCode works with Java, JavaScript, and Python, and its ML algorithms “learn” from thousands of high-quality open-source repositories. This tool can analyze the intent of the code. You can use DeepCode on the cloud, alternatively, you can install it on-premise.

Let‘s take an example to understand how this goes well beyond simple rule-based automation programs. As you know, finding crucial security vulnerabilities like cross-site scripting (XSS) and SQL injection can be hard with traditional code review tools. DeepCode can find these types of vulnerabilities, and that‘s a very important capability.

applitools

You know visual testing and monitoring requires a significant manual effort, e.g., you need to configure various parameters in the testing framework. Your team needs to modify visual processing settings, and they need to do this for multiple platforms.

With applitools, an ML-powered testing tool, it‘s very different! Its ML-algorithms are adaptive, therefore, you don‘t need to do those manual configurations. With this visual UI testing and monitoring tool, you can find potential bugs without explicitly specifying elements.

Applitools isn‘t the only such ML-powered testing tools. You can find more such examples in “8 innovative AI test automation tools for the future: the third wave”.

Planning to transform software development in your organization with the help of machine learning?

There are various ways in which ML can change software development in your organization, however, it can be hard to build such ML-powered solutions. ML is a niche skill, and building such a solution is a complex project. I recommend that you engage a reputed software development company for such projects, and read our guide “How to find the best software development company?” to find one.

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Aran Davies

Blockchain Expert | Developer | Writer | Photographer
Aran Davies

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