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Using machine learning algorithms for measuring performance

As the saying goes, "You can't manage what you can't measure." This is especially true in software development, where the success of a project hinges on the performance of the development team. But fear not! Machine learning is here to help.

As the saying goes, "You can't manage what you can't measure." This is especially true in software development, where the success of a project hinges on the performance of the development team. But fear not! Machine learning is here to help. With its powerful algorithms and ability to crunch through vast amounts of data, machine learning can provide a much more accurate picture of your team's performance. So, grab a cup of coffee and let's dive into the four ways machine learning can help measure your software development team's performance.

Predictive analytics is a branch of data analytics that uses machine learning and statistical algorithms to analyze past data and predict future outcomes. It involves the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data patterns.

Predictive analytics can also be applied in software development teams to predict project outcomes, identify potential risks, and optimize team performance. Here are some examples of how predictive analytics can be used specifically in software development teams:

1. **Predicting project completion time:** By analyzing past project data, machine learning algorithms can predict the time required to complete future projects. This can help software development teams better plan their work and allocate resources more efficiently.
2. **Identifying potential risks:** By analyzing historical data and identifying patterns, machine learning algorithms can predict potential risks in a project. This can help development teams take proactive measures to mitigate those risks and avoid project delays or failures.
3. **Improving software quality:** Machine learning algorithms can analyze historical data on code quality, test coverage, and defect rates to predict the quality of future code changes. This can help development teams identify potential quality issues before they occur and improve the overall quality of their software.
4. **Predicting team performance:** By analyzing historical team data, machine learning algorithms can predict team performance, identify areas for improvement, and recommend coaching or training to team members. This can help development teams optimize their performance and achieve better results.

using DORA Metrics for performance predictions

Predictive analytics machine learning can help software development teams make more informed decisions, optimize their performance, and improve the quality of their software. As data becomes increasingly important in software development, the use of predictive analytics is likely to become even more widespread.

Time tracking involves using machine learning algorithms to track the time spent on each task or feature. This information can be used to identify areas where the team may be spending too much time, allowing organizations to allocate resources more efficiently.

1. **Time estimation:** Machine learning algorithms can analyze historical data from similar projects to estimate the time required for specific tasks. This can help development teams plan more accurate project schedules and allocate resources more efficiently.
2. **Time allocation:** Machine learning algorithms can analyze time-tracking data to identify where team members are spending the most time. This information can help development teams identify areas where more resources may be needed and allocate resources more effectively.
3. **Productivity monitoring:** Machine learning algorithms can analyze time-tracking data to monitor individual team member productivity. This information can help development teams identify where team members may need additional coaching or training to improve their performance.
4. **Task prioritization:** Machine learning algorithms can analyze time-tracking data to identify which tasks are taking the most time to complete. This information can help development teams prioritize their work and make adjustments to ensure that the most important tasks are completed first.
5. **Resource planning:** Machine learning algorithms can analyze time-tracking data to identify which team members are working most efficiently and allocate resources accordingly. This can help development teams optimize their resource allocation and improve overall team performance.

 machine learning enhanced time-tracking in Agile Analytics

Time-tracking machine learning algorithms can provide valuable insights into team performance and help development teams make data-driven decisions to improve their productivity and meet project deadlines. 

Resource allocation machine learning algorithms can provide valuable insights into how development teams can allocate resources more effectively to improve project outcomes. As software development projects become increasingly complex, resource allocation is likely to become even more critical for ensuring project success, and the use of resource allocation machine learning algorithms is likely to become increasingly common.

Sentiment analysis is a powerful application of machine learning that allows software development teams to analyze customer feedback, identify patterns and insights, and improve the quality of their software.

Here are some examples of how sentiment analysis can be used specifically in software development teams:

1. **Analyzing customer feedback:** By analyzing customer feedback through social media, email, and other channels, machine learning algorithms can identify common issues and complaints. This can help development teams identify areas for improvement and prioritize their work accordingly.
2. **Identifying user experience issues:** Machine learning algorithms can analyze user feedback to identify patterns in how users are interacting with the software. This can help development teams identify areas where user experience could be improved and make adjustments accordingly.
3. **Monitoring software reviews:** Machine learning algorithms can monitor online software reviews to identify common complaints and issues. This can help development teams prioritize their work and make improvements to their software based on customer feedback.
4. **Analyzing support tickets:** Machine learning algorithms can analyze support tickets to identify common issues and the frequency of those issues. This can help development teams prioritize their work and make improvements to their software to reduce the number of support tickets over time.
5. **Identifying sentiment in code reviews:** Machine learning algorithms can analyze code reviews to identify sentiment and the overall tone of comments. This can help development teams identify areas where team members may need additional coaching or training and improve the overall quality of their code.

Machine learning-based sentiment analysis can assist software development teams in gaining a better understanding of their customers, recognizing the areas where improvements are required, and making well-informed decisions backed by data to enhance the overall quality of their software.

In conclusion, with machine learning, you can say goodbye to subjective performance evaluations and hello to accurate data-driven insights. It's like having a team of highly skilled analysts working tirelessly in the background, except they don't take coffee breaks or nap time. By using machine learning algorithms, you can **optimize your team's performance, make informed decisions, and deliver high-quality software products**. So what are you waiting for? Embrace the power of machine learning and take your software development team's performance to new heights!

Agile Analytics now support AWS Cloud Watch

- Choose AWS Cloud Watch
- Configure the details and press ‘test’
- Then under ‘SRE & Error Budget’ you can add a feature to a service and configure the cloud watch metrics copied from the Cloud Watch Console

Configure the details and press ‘test’

Then under ‘SRE & Error Budget’ you can add a feature to a service and configure the cloud watch metrics copied from the Cloud Watch Console

Press ‘create’

here is an example of the code you need to copy from Cloud watch

Once configured you’ll see the error budgets managed for your teams

For more information, please reach out to [ZEN Software’s Support](https://www.agileanalytics.cloud/contact?slug=contact).

What is Agile Analytics?

Agile Analytics is our amazing platform for optimizing software operations. The platform allows teams to gain insights into their entire pipeline and integrates against all the tools used rather than being limited to Jira or Git. Agile Analytics integrates various systems like your project management system (Jira, Azure DevOps), monitoring systems (AWS CloudWatch, GCP Cloud Monitor, Azure Monitor),  collaborative communication systems (Slack, Teams or Discord), and continuous delivery (like Github, Gitlab or Bit Bucket). 

Agile Analytics is our amazing platform for optimizing software operations. The platform allows teams to gain insights into their entire pipeline and integrates against all the tools used rather than being limited to Jira or Git. Agile Analytics integrates various systems like your project management system (Jira, Azure DevOps), monitoring systems (AWS CloudWatch, GCP Cloud Monitor, Azure Monitor),  collaborative communication systems (Slack, Teams or Discord), and continuous delivery (like Github, Gitlab or Bit Bucket). Using this platform, teams can make more informed decisions and become data-driven!

How to measure productivity metrics of software teams

Understanding team performance is crucial for professionals in the tech industry, regardless of their role. It can be challenging to get a clear view of how teams are performing, but with the right tools and strategies, professionals can take their team's performance to the next level and achieve business goals more effectively. Practical tips include increasing team autonomy, balancing maintenance and feature work, and getting a grip on software development stock.

Assessing software development progress and its impact on production can be difficult, but there are tools and metrics available to gain a better understanding of team performance. Manually reviewing backlog items can provide insight, but automation is necessary for regular analysis. Objective metrics like Error Budgets can signal when to shift focus. The right metrics need to be identified to measure productivity accurately, such as DevOps DORA Metrics. Team culture is also important to consider, with tools like Kudos to gauge overall morale. Agile Analytics provides features to optimize workflows, measure team productivity using DORA metrics, and ensure a safe, fun, and engaging work environment.

['Error Budgets'](https://www.agileanalytics.cloud/features#errorbudgets) are a way to measure and manage the allowable amount of errors or downtime in a system or service. It involves setting a threshold for acceptable errors, and the remaining budget is used for making improvements or implementing preventative measures. This approach helps teams prioritize their efforts and resources to minimize the impact of errors and keep the system or service running smoothly.

Using Error Budgets, teams can automatically monitor their performance and receive alerts when their service level drops too low. This feature increases team autonomy and provides valuable insights into the quality of their service delivery.

- Monitors if your service level is inside or outside of 'budget'
- Improves the autonomy of your agile team
- Delivers actionable data about how you’re managing production

[Sprint Insights](https://www.agileanalytics.cloud/features#sprintinsights) is a feature that uses deep learning technology to determine whether a ticket represents **maintenance** or **feature work** and how much time is spent on each. This feature provides users with greater insight into their team's sprints, allowing them to make more data-driven decisions and focus on the areas that matter most. By analyzing millions of tickets from public sources, Sprint Insights provides accurate and comprehensive information, making it a valuable tool for teams looking to improve their productivity and efficiency.

- Creates insights in feature development balance
- Uses AI and machine learning to analyze your tickets
- Automatically registers the time spent on feature & non-feature work

[The Stocks](https://www.agileanalytics.cloud/features#stock) feature helps users manage their software development backlog, also known as their software stock. The Stocks feature in software development helps manage the backlog by tracking deployment cadence and identifying waste. It provides insights into the amount of work waiting to be released and helps identify bottlenecks and inefficiencies. This tool is helpful for improving the software delivery process, optimizing resources, and reducing costs associated with a high backlog. It shows unexecuted development changes, the productivity of the team, and relevant data about software stock and waste.

[The DORA metrics](https://www.agileanalytics.cloud/manage-devops-using-dora-metrics) are a set of performance metrics developed by Google's DevOps Research and Assessment team to help software development teams measure and improve their performance. These metrics are based on six years of research and are predictive of software delivery operational performance. The DORA metrics provide insights into successful deployments, end-to-end monitoring of feature realization, lead time for delivering value, service level, change failure rate, and operational productivity metrics. Teams can track these metrics continuously and automatically using the Agile Analytics platform. The DORA metrics feature is a valuable tool for any software development team looking to improve their performance and deliver better software faster. It provides clear and fair performance data, streamlines development processes, and creates happier developers while increasing engagement and motivation.

The Lost API Key feature in Agile Analytics is designed to help users scan their Git repositories for any lost API or private keys. If a lost key is found, users can quickly identify what happened, block the lost key, and resolve the incident, ensuring the safety and security of their systems.

This feature is particularly valuable for teams working on large software development projects where many keys may be in use, making it difficult to keep track of them all. By using Agile Analytics, users can quickly scan their repositories and identify any lost keys, preventing potential security breaches and ensuring the smooth operation of their systems.

- Get a clear insight into your teams’ software waste
- Figure out where software stock is created
- Automatically scan your Git repositories on ‘lost’ keys
- Easily mark the key and solve the incident

The Lost API Key feature is an important tool for teams looking to ensure the safety and security of their systems, particularly in high-risk environments where sensitive data may be at risk. With Agile Analytics, users can quickly identify and resolve incidents related to lost API or private keys, making it easier to maintain the integrity of their systems and safeguard their data.

The hour registration feature in the Agile Analytics platform allows users to quickly and easily register the number of hours worked. The platform also includes an inferred hours feature that suggests the number of hours worked based on the user's input. This all-in-one platform feature can help users keep track of their work hours accurately and efficiently.

The feature allows users to reward their team members with Kudos and celebrate their personal and team achievements. It also provides leaderboards to keep track of scores and encourage friendly competition. Overall, the feature promotes a fun and positive team dynamic and encourages collaboration and recognition among team members.

- Celebrate team achievements together.
- Reward each other with Kudos
- Have a little fun and recognize each other! (:balloon: , :star:, :+1:)

Are you tired of guesswork and unproductive software development cycles? Look no further than Agile Analytics! By integrating with all existing support tools, our platform provides critical insights into the health of your software operations and where to improve. With features like Error Budget, teams will know exactly when to push forward on feature development or break a sprint. Our Deep Learning-powered dashboards empower software teams with the right metrics to become high-performing, all while reducing defects in production and cutting feature delivery time. 

**Say goodbye to unproductive cycles and hello to Agile Analytics!**

Visit our [website](https://www.agileanalytics.cloud/) or contact us at <hello@agileanalytics.cloud> to learn more.

The effects of Machine Learning on your site's search bar

Personally, I don’t like the so-called “Silicon Valley Inspirational trips”. Don’t get me wrong: they sound like fun but going there just for inspiration seems a bit forced. Then: what do you call it when you happen to be in San Francisco, you are in the software business and you get ‘inspired’?  :wink:

This is what happened to me when I was in San Francisco in April 2019 to give a talk about Maxeda, Cloud Transformation and all the cool things we did there. “Since we’re already in SF” we decided to meet up with the cool team from [Constructor.io](https://constructor.io/). (see photo)

Personally, I don’t like the so-called “Silicon Valley Inspirational trips”. Don’t get me wrong: they sound like fun but going there just for inspiration seems a bit forced. Then: what do you call it when you happen to be in San Francisco, you are in the software business and you get ‘inspired’?  :wink:

This is what happened to me when I was in San Francisco in April 2019 to give a talk about Maxeda, Cloud Transformation and all the cool things we did there. “Since we’re already in SF” we decided to meet up with the cool team from [Constructor.io](https://constructor.io/). (see photo)

[Constructor.io](https://constructor.io/). is a Search ML startup based in downtown San Francisco. Their products enable e-commerce platforms to provide a better search experience for their users. [Constructor.io](https://constructor.io/). uses Machine Learning to train the site on what certain products ‘mean’ when they search for keywords. Sounds promising, let’s see what they can tell us.

We had a great time and left knowing a lot more about why Machine Learning is crucial to e-commerce platforms. “be more like Google when you’re doing search” was inspiring to us: we faced challenges with a lot of manual work for the ±100k products that need to be online. Automating ranking based on customer behaviour is crucial because ‘human brain resources’ are always limited!

**Update:** Nov 2020. After a smooth implementation project, the new constructor.io search engine is powering this [large regional DIY retailer](https://www.praxis.nl/)’s online platform.

**Picture Left (before situation)**: no Machine Learning (ML) is used in the search. Instead: Literal text search is used by the product search engine of the (now old) site. no good support for compounded words. Dutch (like German & Turkish; compound words. ‘glues them together’). In this example, we are looking for a **Garden Shed**. 

This is 1 word in Dutch but is glued together out of 2 words: Garden and House. if you search for the 2 individual words you get very bad results. (**Left**). No sheds, just wall paper from a brand that has the 2 individual words as brand name, that fact ranks the wallpaper products higher. 

When using Machine Learning, ‘the Machine’ (the site) learns to compensate for this human error. By observing the behaviour of users searching for products and then clicking on a certain product and buying certain products, the machine learns what words mean.  

The result, the picture **on the right (the after situation)** is that you get the 100% match for the Garden Sheds. 

A second scenario describes the ‘before’ and ‘after’ situations when searching for a hammer. **On the left, the ‘before’ situation** shows products where 'hammer’ is in the title of the product. safety hammer, wooden hammer and a drill. Obviously, these are not the products we need when we are looking for a hammer. 

When implementing a Machine Learning search, the product in the **picture on the right** is good results for hammers. 

Implementing Machine Learning product search does miracles for conversion, and user experience and saves *a lot* of time of Product Managers improving search queries by hand. In the old scenario finding better converting synonyms was **manua**l work, highly error-prone and simply too much work with around 100k products online. Machine Learning Search is at the core of the world-beating success of Google (Since the early 2000s). It is soon the *de facto* standard in all search engines. 


How ChatGPT can enhance your software development team performance

Software development can be tough. With tight deadlines, many lines of code to write, and countless bugs to fix, it can feel like a never-ending uphill battle. But thankfully, ChatGPT is coming to the rescue to level up your team’s performance. Luckily ChatGPT is a much better coder than a poet.

Software development can be tough. With tight deadlines, many lines of code to write, and countless bugs to fix, it can feel like a never-ending uphill battle. But thankfully, ChatGPT is coming to the rescue to level up your team’s performance. Luckily ChatGPT is a much better coder than a poet.

ChatGPT is designed to generate natural language responses to user inputs, making it a very valuable tool in a wide range of applications, from language translation to chatbots. Now, let's dive into how your software development team can use ChatGPT to boost performance.

Collaboration is key to the success of any software development team. ChatGPT can be used to enhance collaboration by facilitating communication between team members.  ChatGPT allows team members to communicate effectively, provide real-time feedback on task progress, and collaborate on code and documentation. 

Using our AnalyticsBot as part of your Slack workspace, your teams can now employ chatGPT to suggest implementations or to help out with coding problems. Take this example, for instance:

By mentioning AnalyticsBot, Paul now has a solve for his coding problem.

ChatGPT can be used to improve code quality by analyzing code and identifying areas for improvement. 

ChatGPT can help you improve your code quality by providing a second set of eyes on your code, a.k.a code review. By submitting your code to ChatGPT, the language model can analyze the code and provide suggestions for improvements. For example, it can recommend better variable names, suggest ways to optimize code, or identify potential security vulnerabilities. 

It can also contribute to code refactoring by suggesting improvements to code snippets. ChatGPT can suggest ways to remove duplicate code, recommend better variable or function names, or identify areas where the code can be simplified.

ChatGPT can assist in staying up-to-date with the latest best practices in software development as well. By analyzing your code and comparing it to industry standards, ChatGPT can identify areas where your code can be improved to align with best practices. It can recommend the use of design patterns, suggest the use of automated testing, or highlight areas where code comments could be added etc.

With its ability to estimate project completion times and improve collaboration, ChatGPT is like the sidekick that helps developers become the superhero they always should have been. But like any good sidekick, ChatGPT comes with its quirks and unique personality. Sometimes it may be wrong or give an estimation that's a little off. So we have to be aware of it and not rely on AI completely.

This is where us mere mortals come in; Based on years of experience and with the right robotic tools, ZEN Software’s Agile Analytics and services will supercharge your software operation.

**Want to increase your software development team's productivity? Try Agile Analytics today with our 60-day free trial and watch your performance soar.**

 

ZEN Software releases Agile Analytics Cloud

After 30 months of coding, designing, debating and bugfixing we’re proud and happy to announce we are releasing Agile Analytics to the general public!
 

Agile Analytics is now GA
Why should your organisation use Agile Analytics?

**For Development Managers**
- Match or challenge gut feeling with real data
- Reward collaborative behaviour
- Manage Developer Experience (DX)

**For Teams & Agile Coaches**
- Improve autonomy: Make decisions for yourselves
- OKR /  SLA : promise vs reality
- Actionable & real-time performance data:  ‘how are we really doing?’

After 30 months of coding, designing, debating and bugfixing we’re proud and happy to announce we are releasing Agile Analytics to the general public!

Why should your organisation use Agile Analytics?

**For Development Managers**
- Match or challenge gut feeling with real data
- Reward collaborative behaviour
- Manage Developer Experience (DX)




**For Teams & Agile Coaches**
- Improve autonomy: Make decisions for yourselves
- OKR /  SLA : promise vs reality
- Actionable & real-time performance data:  ‘how are we really doing?’



Agile Analytics integrates with a wide variety of Software Development systems that enables real insight into your software operation 

Measure the Team Spirit using Leaderboards, reward team behaviour

Measure the balance between feature work and non-feature work

How are your team’s services performing? are they performing like the teams promised? 

match SLA/SLO to real-time monitoring systems and see if the team is focusing in ‘the right thing’

Detect previously undetected leaks in your code by scanning the repositories for lost keys and see how much work is 'in process’. How is the software 'stock' situation? are the teams regularly releasing new changes or are queues forming inside your software operation?

Agile Analytics now supports Sprint Insights


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Agile Analytics allows you to integrate Jira project management to determine what of the completed (or created) tasks qualify as

- feature
- non-feature


ZEN Software created a machine learning classification algorithm to teach Agile Analytics what is a ‘feature’ and what not.
To help improve the accuracy you as user of Agile Analytics can train the model for your organisation.

This great new innovation allows Development Manager and Agile Coaches everywhere to see how the pace of feature dev is! 





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Agile Analytics allows you to integrate Jira project management to determine what of the completed (or created) tasks qualify as

- feature
- non-feature


ZEN Software created a machine learning classification algorithm to teach Agile Analytics what is a ‘feature’ and what not

To help improve the accuracy you as user of Agile Analytics can train the model for your organisation.

This great new innovation allows Development Manager and Agile Coaches everywhere to see how the pace of feature dev is! 

Also it saves a lot of time running down ticket lists and pondering what tasks are ‘feature’ and what not.
