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One of our patents is getting referenced by others

We just realized that one of our patents, US Patent No. 7,958,080, is getting referenced by other inventors:

Patent Applicant Title
US8214313 Ford Motor Company Turn rate calculation
US8489647 IBM Use-case based configuration of an object-relational mapping framework
US8543785 Microsoft Protocol for managed copy of media content
US20080028170 Microsoft Protocol for Managed Copy of Media Content
US20110137763 Dirk Aguilar System that Captures and Tracks Energy Data for Estimating Energy Consumption, Facilitating its Reduction and Offsetting its Associated Emissions in an Automated and Recurring Fashion
US20120054227 IBM Use-case based configuration of an object-relational mapping framework

Well, this is interesting…

 

 

Sorting by Date

We have added a couple of new features related to dates:

  • Every card in the Done column, of a Task Board or Scrum Board, will show the date on which the card was marked as done: this makes it easy to see, at a glance, when work was completed on a project.
  • Cards that have dates assigned to them can get sorted by date.

If a column contains any cards with dates assigned to them, a “Sort by Date” button appears at the top of the column:

Sort by Date button
Sort by Date button

Clicking on this button will sort the cards that have dates:

  • Only cards with dates are affected: if a column contains some cards that don’t have dates, these are not affected.
  • You can sort in ascending or descending order.

This is a useful feature for date-driven projects, but if you are working in a pure Kanban or Scrum team, you might want to stick with (manually) sorting dates by priority, which the highest priority items at the top of the column.

Something better than Google Contacts

We are replacing our integration with Google Docs with a “friends of friends” model.

The Background:

For the past 2 years, Kerika has offered an “auto-completion” feature that let you type just a few characters of someone’s name, and then have a list of matching names and emails appear from your Google Docs. It looked like this:

Auto-completion of invitations
Auto-completion of invitations

This was actually a very helpful feature, but it was also scaring off too many potential users.

The Problem:

When you sign up as a new Kerika user, Google asks whether it is OK for Kerika to “manage your Google Contacts”. This was a ridiculous way to describe our actual integration with Google Contacts, but there wasn’t anything we could do about this authorization screen.

We lost a lot of potential users thanks to this: people who had been burned in the past by unscrupulous app developers who would spam everyone in their address book. So, we concluded that this cool feature was really a liability.

The Solution:

We are abandoning integration with Google Contacts with our latest software update. Existing users are not affected, since they have already authorized Kerika to access their Google Contacts (and are, presumably, comfortable with that decision), but new users will no longer be asked whether it is OK for Kerika to “manage their Google Contacts”.

Instead, we are introducing our own auto-completion of names and email addresses based upon a friends of friends model: if you type in part of a user’s email, Kerika will help you match this against the names of that are part of your extended collaboration network:

  • People you already work with on projects.
  • People who work with the people who work with you.

We hope this proves to be a more comfortable fit for our users; do let us know what you think!

Breaking through the code review bottleneck

A month ago we wrote about how Kerika makes it really easy to spot bottlenecks in a development process – far easier, in our opinion – than relying upon burndown carts.

That blog post noted that the Kerika team itself had been struggling with code reviews as our major bottleneck. Well, we are finally starting to catch up: over the past two days we focused heavily on code reviews and just last night nearly 80 cards got moved to Done!

Getting Done

What a CPU spike looks like

We have been experiencing a CPU spike on one of our servers over the past week, thanks to a batch job that clearly needs some optimization.

The CPU spike happens at midnight, UTC (basically, Greenwich Mean Time), when the job was running, and it looks like this:

CPU spike
CPU spike

It’s pretty dramatic: our normal CPU utilization is very steady, at less than 30%, and the over a 10-minute period at midnight it shoots up to nearly 90%.

Well, that sucks. We have disabled the batch job and are going to take a closer look at the SQL involved to optimize the code.

Our apologies to users in Australia and New Zealand: this was hitting the middle of their morning use of Kerika and some folks experienced slowdowns as a result.

How we manage our Bugs Backlog

Talk to old-timers at Microsoft, and they will wax nostalgic about Windows Server 2003, which many of the old hands describe as the best Windows OS ever built. It was launched with over 25,000 known bugs.

Which just goes to show: not all bugs need to be fixed right away.

Here at Kerika we have come up with a simple prioritization scheme for bugs; here’s what our board for handling server-related bugs looks like:

How we prioritize errors
How we prioritize errors (click to enlarge)

This particular board only deals with exceptions logged on our servers; these are Java exceptions, so the cards may seem obscure in their titles, but the process by which we handle bugs may nonetheless be of interest to others:

Every new exception goes into a To be Prioritized column as a new card. Typically, the card’s title includes the key element of the bug – in this case, the bit of code that threw the exception – and the card’s details contain the full stack trace.

Sometimes, a single exception may manifest itself with multiple code paths, each with its own stack trace, in which case we gather all these stack traces into a single Google Docs file which is then attached to the card.

With server exceptions, a full stack trace is usually sufficient for debugging purposes, but for UI bugs the card details would contain the steps needed to reproduce the bug (i.e. the “Repro Steps”).

New server exceptions are found essentially randomly, with several exceptions being noted in some days and none in other days.

For this reason, logging the bugs is a separate process from prioritizing them: you wouldn’t want to disturb your developers on a daily basis, by asking them to look at any new exceptions they are found, unless the exceptions point to some obviously serious errors. Most of the time the exceptions are benign, and perhaps annoying, rather than life-threatening, so we ask the developers to examine and prioritize bugs from the To be Prioritized column only as they periodically come up for air after having tackled some bugs.

Each bug is examined and classified as either High Priority or Ignore for Now.

Note that we don’t bother with a Medium Priority, or, worse yet, multiple levels of priorities (e.g. Priority 1, Priority 2, Priority 3…). There really isn’t any point to having more than two buckets in which to place all bugs: it’s either worth fixing soon, or not worth fixing at all.

The rationale for our thinking is simple: if a bug doesn’t result in any significant harm, it can usually be ignored quite safely. We do about 30 cards of new development per week (!), which means we add new features and refactor our existing code at a very rapid rate. In an environment characterized by rapid development, there isn’t any point in chasing after medium- or low-priority bugs because the code could change in ways that make these bugs irrelevant very quickly.

Beyond this simple classification, we also use color coding, sparingly, to highlight related bugs. Color coding is a feature of Kerika, of course, but it is one of those features that needs to be used as little as possible, in order to gain the greatest benefit. A board where every card is color-coded will be a technicolor mess.

In our scheme of color coding, bugs are considered “related” if they are in the same general area of code, which provides an incentive for the developer to fix both bugs at the same time since the biggest cost of fixing a bug is the context switch needed for a developer to dive into some new part of a very large code base. (And we are talking about hundreds of thousands of lines of code that make up Kerika…)

So, that’s the simple methodology we have adopted for tracking, triaging, and fixing bugs.

What’s your approach?