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Agile Analytics now supports Sprint Insights

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! 



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.


Machine-learningalgoritmen gebruiken om prestaties te meten

Zoals het gezegde luidt: "You can't manage what you can't measure". Dit geldt vooral voor softwareontwikkeling, waar het succes van een project afhangt van de prestaties van het ontwikkelteam. Maar wees niet bang! Machine learning is er om te helpen.

Zoals het gezegde luidt: 'You can't manage what you can't measure'. Dit geldt vooral voor softwareontwikkeling, waar het succes van een project afhangt van de prestaties van het ontwikkelteam. Maar wees niet bang! Machine learning staat klaar om te helpen. Dankzij krachtige algoritmes en het vermogen om enorme hoeveelheden gegevens te verwerken, kan machine learning een veel nauwkeuriger beeld geven van de prestaties van je team. Dus pak een kop koffie en laten we ons verdiepen in de vier manieren waarop machine learning kan helpen om de prestaties van je softwareontwikkelingsteam te meten.

Voorspellende analyse is een tak van data-analyse die machine learning en statistische algoritmen gebruikt om gegevens uit het verleden te analyseren en toekomstige uitkomsten te voorspellen. Het omvat het gebruik van gegevens, statistische algoritmen en machine-learningtechnieken om de waarschijnlijkheid van toekomstige uitkomsten te identificeren op basis van historische gegevenspatronen.

Voorspellende analyses kunnen ook worden toegepast in softwareontwikkelingsteams om projectresultaten te voorspellen, potentiële risico's te identificeren en teamprestaties te optimaliseren. Hier zijn enkele voorbeelden van hoe predictive analytics specifiek kan worden gebruikt in softwareontwikkelingsteams:

1. **Voorspellen van de voltooiingstijd van projecten:** Door projectgegevens uit het verleden te analyseren, kunnen machine learning-algoritmen voorspellen hoeveel tijd nodig is om toekomstige projecten af te ronden. Dit helpt softwareontwikkelingsteams bij het verbeteren van hun projectplanning en het efficiënter toewijzen van resources.
2. **Identificeren van potentiële risico's:** Door historische gegevens te analyseren en patronen te identificeren, kunnen machine learning-algoritmen potentiële risico's in een project voorspellen. Dit stelt ontwikkelteams in staat proactieve maatregelen te nemen om die risico's te beperken en projectvertragingen of -mislukkingen te voorkomen.
3. **Verbeteren van de softwarekwaliteit:** Machine learning-algoritmen kunnen historische gegevens over de kwaliteit van code, testdekking en defectpercentages analyseren om de kwaliteit van toekomstige codewijzigingen te voorspellen. Dit helpt ontwikkelteams potentiële kwaliteitsproblemen te identificeren voordat ze zich voordoen en de algehele kwaliteit van hun software te verbeteren.
4. **Voorspellen van teamprestaties:** Door historische teamgegevens te analyseren, kunnen machine learning-algoritmen teamprestaties voorspellen, verbeterpunten identificeren en coaching of training voor teamleden aanbevelen. Dit helpt ontwikkelteams hun prestaties te optimaliseren en betere resultaten te behalen.

DORA-kenmerken gebruiken voor prestatievoorspellingen

Voorspellende analyses en machine learning kunnen softwareontwikkelingsteams helpen bij het nemen van beter geïnformeerde beslissingen, het optimaliseren van hun prestaties en het verbeteren van de kwaliteit van hun software. Nu gegevens steeds belangrijker worden in de context van softwareontwikkeling, is de verwachting dat het gebruik van predictive analytics nog verder zal toenemen.

Bij tijdregistratie worden machine learning-algoritmen ingezet om de tijd bij te houden die aan elke taak of functie wordt besteed. Deze informatie is waardevol om gebieden te identificeren waar het team mogelijk te veel tijd besteedt, waardoor organisaties middelen efficiënter kunnen toewijzen.

1. **Tijdsinschatting:** Machine learning-algoritmen analyseren historische gegevens van vergelijkbare projecten om de benodigde tijd voor specifieke taken in te schatten. Dit helpt ontwikkelteams bij het maken van nauwkeurigere projectplanningen en het efficiënter toewijzen van middelen.
2. **Tijdtoewijzing:** Machine learning-algoritmen analyseren gegevens over tijdregistratie om vast te stellen waar teamleden de meeste tijd besteden. Deze informatie helpt ontwikkelteams bij het identificeren van gebieden waar meer resources nodig zijn en bij het effectiever toewijzen van resources.
3. **Productiviteitsmonitoring:** Machine learning-algoritmen analyseren time-trackinggegevens om de productiviteit van individuele teamleden te monitoren. Deze informatie ondersteunt ontwikkelteams bij het bepalen waar teamleden extra coaching of training nodig hebben om hun prestaties te verbeteren.
4. **Taakprioritering:** Machine learning-algoritmen analyseren time-trackinggegevens om vast te stellen welke taken de meeste tijd kosten om te voltooien. Deze informatie helpt ontwikkelteams bij het prioriteren van hun werk en het maken van aanpassingen om ervoor te zorgen dat de belangrijkste taken als eerste worden voltooid.
5. **Resource planning:** Machine learning-algoritmen analyseren time-trackinggegevens om vast te stellen welke teamleden de meeste tijd nodig hebben om hun taken uit te voeren.

met machine learning verbeterde tijdregistratie in Agile Analytics

Machine learning-algoritmen voor tijdregistratie kunnen waardevolle inzichten verschaffen in teamprestaties en ontwikkelingsteams helpen datagestuurde beslissingen te nemen om hun productiviteit te verbeteren en projectdeadlines te halen.

Machine learning-algoritmen voor resource-toewijzing kunnen waardevolle inzichten verschaffen in hoe ontwikkelteams resources effectiever kunnen toewijzen om de projectresultaten te verbeteren. Naarmate softwareontwikkelingsprojecten steeds complexer worden, zal resource-toewijzing waarschijnlijk nog belangrijker worden voor het succes van het project, en het gebruik van machine learning-algoritmen voor resource-toewijzing zal waarschijnlijk steeds gebruikelijker worden.

Sentimentanalyse is een krachtige toepassing van machine learning waarmee softwareontwikkelingsteams feedback van klanten kunnen analyseren, patronen en inzichten kunnen identificeren en de kwaliteit van hun software kunnen verbeteren.

Hier volgen enkele voorbeelden van hoe sentimentanalyse specifiek kan worden gebruikt in softwareontwikkelingsteams:

1. **Feedback van klanten analyseren:** Door feedback van klanten via sociale media, e-mail en andere kanalen te analyseren, kunnen algoritmen voor machinaal leren veelvoorkomende problemen en klachten identificeren. Dit kan ontwikkelteams helpen om verbeterpunten te identificeren en hun werk dienovereenkomstig te prioriteren.
2. **Gebruikerservaringsproblemen identificeren:** Machine learning-algoritmen kunnen gebruikersfeedback analyseren om patronen te identificeren in de manier waarop gebruikers met de software omgaan. Dit kan ontwikkelteams helpen om gebieden te identificeren waar de gebruikerservaring kan worden verbeterd en dienovereenkomstig aanpassingen door te voeren.
3. **Het monitoren van softwarebeoordelingen:** Machine learning-algoritmen kunnen online softwarebeoordelingen monitoren om veelvoorkomende klachten en problemen te identificeren. Dit kan ontwikkelteams helpen om prioriteiten te stellen en hun software te verbeteren op basis van feedback van klanten.
4. **Ondersteuningstickets analyseren:** Machine learning-algoritmen kunnen ondersteuningstickets analyseren om veelvoorkomende problemen en de frequentie ervan te identificeren. Dit kan ontwikkelteams helpen om prioriteiten te stellen en verbeteringen in hun software aan te brengen om het aantal supporttickets na verloop van tijd te verminderen.
5. **Het identificeren van sentiment in codebeoordelingen:** Machine learning-algoritmen kunnen codebeoordelingen analyseren om sentiment en de algemene toon van opmerkingen te identificeren. Dit kan ontwikkelteams helpen om gebieden te identificeren waar teamleden extra coaching of training nodig hebben en om de algehele kwaliteit van hun code te verbeteren.

Sentimentanalyse op basis van machine learning kan softwareontwikkelingsteams helpen om hun klanten beter te begrijpen, de gebieden te herkennen waar verbeteringen nodig zijn en goed geïnformeerde beslissingen te nemen op basis van gegevens om de algemene kwaliteit van hun software te verbeteren.

Kortom, met machine learning kun je afscheid nemen van subjectieve prestatie-evaluaties en hallo zeggen tegen accurate gegevensgestuurde inzichten. Het is alsof je een team hebt van hoogopgeleide analisten die onvermoeibaar op de achtergrond werken, behalve dat ze geen koffiepauzes of dutjes doen. Door algoritmen voor machinaal leren te gebruiken, kun je de prestaties van je team optimaliseren, weloverwogen beslissingen nemen en softwareproducten van hoge kwaliteit afleveren**. Dus waar wacht je nog op? Omarm de kracht van machine learning en til de prestaties van je softwareontwikkelingsteam naar nieuwe hoogten!

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.**

 

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