As the AI landscape continues to change shape, one thing remains clear: the way that engineering teams work is going to be significantly augmented by copilots. To date, the innovation around software engineer copilots has been mainly focused on code creation, ie how can we write more lines of code, with fewer people (see github copilot). This is what has led to the term of “cyborg” developers, the 100xers that are able to build more than entire full feature teams.
But with the introduction of these technologies and the increasing demands for faster delivery times, maintaining code quality has become more challenging than ever. Sure, you can write more, but how are you ensuring that what you’re writing does not have a broader impact that expands past your knowledge base?
This is the delicate balance that engineering leadership has had to strike when adding new AI tools to the productivity stack.
Enter PlayerZero. PlayerZero is what can be described as the quality counterpart to tools like Github copilot. PlayerZero parses through every code change, and ties it directly to the downstream ripple effects of that change. This gives developers the green light to write quickly, knowing that PlayerZero will keep a lookout for unwanted regressions or other high risk areas
How does PlayerZero work?
Like every AI, ever, it starts with data. PlayerZero's AI is trained off some combination of your team's SCM, telemetry, and ticketing data. PlayerZero creates connections between your work(code) and the experiences of your customers. Say you change a line of code… do you know if anyone created an issue when this code was changed previously? If so, do you know who was affected and for how long? It is these questions that rely on an amount of information and that only a few people in the organization are able to storytell around. PlayerZero unlocks this narrative for anyone to leverage, turning what used to be one person’s work into a wealth of knowledge for the entire organization, now and in the future.
What does PlayerZero unlock for your team?
Code quality is not owned by any one type of person in an organization, it spans multiple disciplines. Support, QA, and customers all act as inputs to driving better quality output, but it always goes through engineering. So we asked ourselves, how can we not only give developers the tools they need to ensure quality, but also the relevant counterparts? How can we give all relevant stakeholders the best chance to successfully communicate with one another and expedite time to detection and resolution? In order to achieve this, we identified the three highest leverage quality processes that PlayerZero’s AI can automate for your team:
QA regression test planning
Manual developer testing
Customer support fix confirmation
Each of these workflows take a tremendous amount of time to execute. They often drag engineering away from the CLI into Zoom meetings, forcing them to context switch in order to educate or provide insight. This is an unbelievable waste of valuable resources.
What’s included in an AI Code Review?
When a developer pushes a change, each of the above workflows kick into motion.
Reflecting back to the aforementioned connection that PlayerZero makes between code and customer signals, PlayerZero is able to tell a complete narrative around the change and its potential impact:
Summarization: PlayerZero starts by summarizing the code changes in the PR and provides a high-level overview in simple human language. This helps the reviewer, as well as any less fluent members of the team, to quickly understand the changes and the impact on the user’s experience.
Dependencies: PlayerZero surfaces all related objects, functions, classes, data, structures that touch this change, creating a directional map of how this one change might impact other implementations of the same asset.
Risk assessments: PlayerZero analyzes the risk of specific code changes on the key product features, as well as at-risk power users for each of those areas of the product.
Regression detection: PlayerZero digs into historical data to pinpoint instances this code has changes in the past and communicates all potential regressions
Interactive checklists: To easily track, triage, and resolve these issues, all items can be managed through the PlayerZero platform or directly in the native pull request. Bi-directional integrations gives anyone on your team access to rich PlayerZero insights wherever you work.
How it fits seamlessly into existing workflows
PlayerZero lives where you work. As earlier stated, we understand that there are a lot of stakeholders with their hand in the quality bucket. So PlayerZero is committed to connecting desperate tentacles of the organization. A few core examples:
Git repository - Whether you use Github or Azure, you can find an automatically generated checklist of items to review before you push to prod
Slack integration - When your team finishes a sprint and pushes changes to prod, PlayerZero automatically creates a new channel in your Slack instances. In this channel, it will then alert you of any issues that are related to the newly introduced code.
PlayerZero application - For those that have fully adopted the PlayerZero experience, they can see checklists, and code related issues directly in the native dashboard.
Unique challenges of how we built AI code reviews
Creating state-of-the-art code review experiences, we are constantly faced with new challenges that have let to some of our greatest innovations.
Code indexing - Building out the index of the entire code base is a big and important piece for generating code insights. We also have an index on code changes proposed throughout PRs. This is used in debugging workflow (the prompt to take you to the line of code and the journey of arriving towards it is an interesting one), pull request summaries, and should be getting used in copilot as well.
Mapping tickets to code - For every new ticket, we needed to tell a story around why it's happening, and who else it's happening to (or could happen to). In order to do this in a way that helps team's root cause issues, we built out a complex map between what people are saying and the semantics of your codebase.
Code search - Checklists are a core part of the AI code review workflow. To generate high quality checklist items, PlayerZero needs to be able to read through entire codebases, searching for the specific semantic element that connects to what customers are saying. PlayerZero also uses code search as a way to surface related changes in objects, functions, classes, data and structures, helping teams better understand the risk of introducing new code.
Noise reduction - Not all changes are created equal. One core challenge that we've faced and are constantly refining is being able to distinguish meaningful code changes. For example, a code formatting adjustment and a significant architectural change should not be considered on the same risk-plane.
Guidance – To create a more bespoke and meaningful risk profile for each code change, we needed to give a place for users to input more information about what matters to their businesses. With feature guidance, users describe their product's core features so we can better tell a story around the code changes. This not only connects engineer work to real-world customer value, but it unlocks the ability for less-technical stakeholders to participate more meaningfully in the quality assurance process.
What comes out on the other side is a end-to-end quality experience that takes teams from first line of code to delightful product experience.
Conclusion
Predictive is always better than reactive. Anytime your team can hook into an existing knowledge base and leverage them to meaningful impact for your customers, they are setting themselves up for success. PlayerZero redefines this process for your entire engineering organization with it's robust AI code review experience.
As the AI landscape continues to change shape, one thing remains clear: the way that engineering teams work is going to be significantly augmented by copilots. To date, the innovation around software engineer copilots has been mainly focused on code creation, ie how can we write more lines of code, with fewer people (see github copilot). This is what has led to the term of “cyborg” developers, the 100xers that are able to build more than entire full feature teams.
But with the introduction of these technologies and the increasing demands for faster delivery times, maintaining code quality has become more challenging than ever. Sure, you can write more, but how are you ensuring that what you’re writing does not have a broader impact that expands past your knowledge base?
This is the delicate balance that engineering leadership has had to strike when adding new AI tools to the productivity stack.
Enter PlayerZero. PlayerZero is what can be described as the quality counterpart to tools like Github copilot. PlayerZero parses through every code change, and ties it directly to the downstream ripple effects of that change. This gives developers the green light to write quickly, knowing that PlayerZero will keep a lookout for unwanted regressions or other high risk areas
How does PlayerZero work?
Like every AI, ever, it starts with data. PlayerZero's AI is trained off some combination of your team's SCM, telemetry, and ticketing data. PlayerZero creates connections between your work(code) and the experiences of your customers. Say you change a line of code… do you know if anyone created an issue when this code was changed previously? If so, do you know who was affected and for how long? It is these questions that rely on an amount of information and that only a few people in the organization are able to storytell around. PlayerZero unlocks this narrative for anyone to leverage, turning what used to be one person’s work into a wealth of knowledge for the entire organization, now and in the future.
What does PlayerZero unlock for your team?
Code quality is not owned by any one type of person in an organization, it spans multiple disciplines. Support, QA, and customers all act as inputs to driving better quality output, but it always goes through engineering. So we asked ourselves, how can we not only give developers the tools they need to ensure quality, but also the relevant counterparts? How can we give all relevant stakeholders the best chance to successfully communicate with one another and expedite time to detection and resolution? In order to achieve this, we identified the three highest leverage quality processes that PlayerZero’s AI can automate for your team:
QA regression test planning
Manual developer testing
Customer support fix confirmation
Each of these workflows take a tremendous amount of time to execute. They often drag engineering away from the CLI into Zoom meetings, forcing them to context switch in order to educate or provide insight. This is an unbelievable waste of valuable resources.
What’s included in an AI Code Review?
When a developer pushes a change, each of the above workflows kick into motion.
Reflecting back to the aforementioned connection that PlayerZero makes between code and customer signals, PlayerZero is able to tell a complete narrative around the change and its potential impact:
Summarization: PlayerZero starts by summarizing the code changes in the PR and provides a high-level overview in simple human language. This helps the reviewer, as well as any less fluent members of the team, to quickly understand the changes and the impact on the user’s experience.
Dependencies: PlayerZero surfaces all related objects, functions, classes, data, structures that touch this change, creating a directional map of how this one change might impact other implementations of the same asset.
Risk assessments: PlayerZero analyzes the risk of specific code changes on the key product features, as well as at-risk power users for each of those areas of the product.
Regression detection: PlayerZero digs into historical data to pinpoint instances this code has changes in the past and communicates all potential regressions
Interactive checklists: To easily track, triage, and resolve these issues, all items can be managed through the PlayerZero platform or directly in the native pull request. Bi-directional integrations gives anyone on your team access to rich PlayerZero insights wherever you work.
How it fits seamlessly into existing workflows
PlayerZero lives where you work. As earlier stated, we understand that there are a lot of stakeholders with their hand in the quality bucket. So PlayerZero is committed to connecting desperate tentacles of the organization. A few core examples:
Git repository - Whether you use Github or Azure, you can find an automatically generated checklist of items to review before you push to prod
Slack integration - When your team finishes a sprint and pushes changes to prod, PlayerZero automatically creates a new channel in your Slack instances. In this channel, it will then alert you of any issues that are related to the newly introduced code.
PlayerZero application - For those that have fully adopted the PlayerZero experience, they can see checklists, and code related issues directly in the native dashboard.
Unique challenges of how we built AI code reviews
Creating state-of-the-art code review experiences, we are constantly faced with new challenges that have let to some of our greatest innovations.
Code indexing - Building out the index of the entire code base is a big and important piece for generating code insights. We also have an index on code changes proposed throughout PRs. This is used in debugging workflow (the prompt to take you to the line of code and the journey of arriving towards it is an interesting one), pull request summaries, and should be getting used in copilot as well.
Mapping tickets to code - For every new ticket, we needed to tell a story around why it's happening, and who else it's happening to (or could happen to). In order to do this in a way that helps team's root cause issues, we built out a complex map between what people are saying and the semantics of your codebase.
Code search - Checklists are a core part of the AI code review workflow. To generate high quality checklist items, PlayerZero needs to be able to read through entire codebases, searching for the specific semantic element that connects to what customers are saying. PlayerZero also uses code search as a way to surface related changes in objects, functions, classes, data and structures, helping teams better understand the risk of introducing new code.
Noise reduction - Not all changes are created equal. One core challenge that we've faced and are constantly refining is being able to distinguish meaningful code changes. For example, a code formatting adjustment and a significant architectural change should not be considered on the same risk-plane.
Guidance – To create a more bespoke and meaningful risk profile for each code change, we needed to give a place for users to input more information about what matters to their businesses. With feature guidance, users describe their product's core features so we can better tell a story around the code changes. This not only connects engineer work to real-world customer value, but it unlocks the ability for less-technical stakeholders to participate more meaningfully in the quality assurance process.
What comes out on the other side is a end-to-end quality experience that takes teams from first line of code to delightful product experience.
Conclusion
Predictive is always better than reactive. Anytime your team can hook into an existing knowledge base and leverage them to meaningful impact for your customers, they are setting themselves up for success. PlayerZero redefines this process for your entire engineering organization with it's robust AI code review experience.
As the AI landscape continues to change shape, one thing remains clear: the way that engineering teams work is going to be significantly augmented by copilots. To date, the innovation around software engineer copilots has been mainly focused on code creation, ie how can we write more lines of code, with fewer people (see github copilot). This is what has led to the term of “cyborg” developers, the 100xers that are able to build more than entire full feature teams.
But with the introduction of these technologies and the increasing demands for faster delivery times, maintaining code quality has become more challenging than ever. Sure, you can write more, but how are you ensuring that what you’re writing does not have a broader impact that expands past your knowledge base?
This is the delicate balance that engineering leadership has had to strike when adding new AI tools to the productivity stack.
Enter PlayerZero. PlayerZero is what can be described as the quality counterpart to tools like Github copilot. PlayerZero parses through every code change, and ties it directly to the downstream ripple effects of that change. This gives developers the green light to write quickly, knowing that PlayerZero will keep a lookout for unwanted regressions or other high risk areas
How does PlayerZero work?
Like every AI, ever, it starts with data. PlayerZero's AI is trained off some combination of your team's SCM, telemetry, and ticketing data. PlayerZero creates connections between your work(code) and the experiences of your customers. Say you change a line of code… do you know if anyone created an issue when this code was changed previously? If so, do you know who was affected and for how long? It is these questions that rely on an amount of information and that only a few people in the organization are able to storytell around. PlayerZero unlocks this narrative for anyone to leverage, turning what used to be one person’s work into a wealth of knowledge for the entire organization, now and in the future.
What does PlayerZero unlock for your team?
Code quality is not owned by any one type of person in an organization, it spans multiple disciplines. Support, QA, and customers all act as inputs to driving better quality output, but it always goes through engineering. So we asked ourselves, how can we not only give developers the tools they need to ensure quality, but also the relevant counterparts? How can we give all relevant stakeholders the best chance to successfully communicate with one another and expedite time to detection and resolution? In order to achieve this, we identified the three highest leverage quality processes that PlayerZero’s AI can automate for your team:
QA regression test planning
Manual developer testing
Customer support fix confirmation
Each of these workflows take a tremendous amount of time to execute. They often drag engineering away from the CLI into Zoom meetings, forcing them to context switch in order to educate or provide insight. This is an unbelievable waste of valuable resources.
What’s included in an AI Code Review?
When a developer pushes a change, each of the above workflows kick into motion.
Reflecting back to the aforementioned connection that PlayerZero makes between code and customer signals, PlayerZero is able to tell a complete narrative around the change and its potential impact:
Summarization: PlayerZero starts by summarizing the code changes in the PR and provides a high-level overview in simple human language. This helps the reviewer, as well as any less fluent members of the team, to quickly understand the changes and the impact on the user’s experience.
Dependencies: PlayerZero surfaces all related objects, functions, classes, data, structures that touch this change, creating a directional map of how this one change might impact other implementations of the same asset.
Risk assessments: PlayerZero analyzes the risk of specific code changes on the key product features, as well as at-risk power users for each of those areas of the product.
Regression detection: PlayerZero digs into historical data to pinpoint instances this code has changes in the past and communicates all potential regressions
Interactive checklists: To easily track, triage, and resolve these issues, all items can be managed through the PlayerZero platform or directly in the native pull request. Bi-directional integrations gives anyone on your team access to rich PlayerZero insights wherever you work.
How it fits seamlessly into existing workflows
PlayerZero lives where you work. As earlier stated, we understand that there are a lot of stakeholders with their hand in the quality bucket. So PlayerZero is committed to connecting desperate tentacles of the organization. A few core examples:
Git repository - Whether you use Github or Azure, you can find an automatically generated checklist of items to review before you push to prod
Slack integration - When your team finishes a sprint and pushes changes to prod, PlayerZero automatically creates a new channel in your Slack instances. In this channel, it will then alert you of any issues that are related to the newly introduced code.
PlayerZero application - For those that have fully adopted the PlayerZero experience, they can see checklists, and code related issues directly in the native dashboard.
Unique challenges of how we built AI code reviews
Creating state-of-the-art code review experiences, we are constantly faced with new challenges that have let to some of our greatest innovations.
Code indexing - Building out the index of the entire code base is a big and important piece for generating code insights. We also have an index on code changes proposed throughout PRs. This is used in debugging workflow (the prompt to take you to the line of code and the journey of arriving towards it is an interesting one), pull request summaries, and should be getting used in copilot as well.
Mapping tickets to code - For every new ticket, we needed to tell a story around why it's happening, and who else it's happening to (or could happen to). In order to do this in a way that helps team's root cause issues, we built out a complex map between what people are saying and the semantics of your codebase.
Code search - Checklists are a core part of the AI code review workflow. To generate high quality checklist items, PlayerZero needs to be able to read through entire codebases, searching for the specific semantic element that connects to what customers are saying. PlayerZero also uses code search as a way to surface related changes in objects, functions, classes, data and structures, helping teams better understand the risk of introducing new code.
Noise reduction - Not all changes are created equal. One core challenge that we've faced and are constantly refining is being able to distinguish meaningful code changes. For example, a code formatting adjustment and a significant architectural change should not be considered on the same risk-plane.
Guidance – To create a more bespoke and meaningful risk profile for each code change, we needed to give a place for users to input more information about what matters to their businesses. With feature guidance, users describe their product's core features so we can better tell a story around the code changes. This not only connects engineer work to real-world customer value, but it unlocks the ability for less-technical stakeholders to participate more meaningfully in the quality assurance process.
What comes out on the other side is a end-to-end quality experience that takes teams from first line of code to delightful product experience.
Conclusion
Predictive is always better than reactive. Anytime your team can hook into an existing knowledge base and leverage them to meaningful impact for your customers, they are setting themselves up for success. PlayerZero redefines this process for your entire engineering organization with it's robust AI code review experience.
