Tag Archives: Engineering

Code Reviews: Follow the Data

After years of reviewing other people’s code, I’d like to share a couple practices that improve the effectiveness of code reviews.

Why Review Code?

First, why review code at all? There are a few reasons:

  • Catching bugs
  • Enforce stylistic consistency with the rest of the code
  • Finding opportunities to share code across systems
  • Helping new engineers spin up with the team and project
  • Helping API authors see actual problems that API consumers face
  • Maintain the health of the system overall

It seems most people reach for one of two standard techniques when reviewing code; they either review diffs as they arrive or they review every change in one large chunk. We’ve used both techniques, but I find there’s a more effective way to spend code review time.

Reviewing Diffs

It seems the default code review technique for most people is to sign up for commit emails or proposed patches and read every diff as it goes by. This has some benefits – it’s nice to see when someone is working in an area or that a library had a deficiency needing correction. However, on a large application, diffs become blinding. When all you see is streams of diffs, you can lose sight of how the application’s architecture is evolving.

I’ve ended up in situations where every diff looked perfectly reasonable but, when examining the application at a higher level, key system invariants had been broken.

In addition, diffs tends to emphasize small, less important details over more important integration and design risks. I’ve noticed that, when people only review diffs, they tend to point out things like whitespace style, how iteration over arrays is expressed, and the names of local variables. In some codebases these are important, but higher-level structure is often much more important over the life of the code.

Reviewing diffs can also result in wasted work. Perhaps someone is iterating towards a solution. The code reviewer may waste time reviewing code that its author is intending to rework anyway.

Reviewing Everything

Less often, I’ve seen another code review approach similar to reviewing diffs, but on entire bodies of work at a time. This approach can work, but it’s often mindnumbing. See, there are two types of code: core, foundational code and leaf code. Foundational code sits beneath and between everything else in the application. It’s important that it be correct, extensible, and maintainable. Leaf code is the specific functionality a feature needs. It is likely to be used in a single place and may never be touched again. Leaf code is not that interesting, so most of your code review energy should go towards the core pieces. Reviewing all the code in a project or user story mixes the leaf code in with the foundational code and makes it harder see exactly what’s going on.

I think there’s a better way to run code reviews. It’s not as boring, tends to catch important changes to core systems, and is fairly efficient in terms of time spent.

Follow the Data

My preferred technique for reviewing code is to trace data as it flows through the system. This should be done after a meaningful, but not TOO large, body of work. You want about as much code as you can review in an hour: perhaps more than a user story, but less than an entire feature. Start with a single piece of data, perhaps some text entered on a website form. Then, trace that data all the way through the system to the output. This includes any network protocols, transformation functions, text encoding, decoding, storage in databases, caching, and escaping.

Following data through the code makes its high-level structure apparent. After all, code only exists to transform data. You may even notice scenarios where two transformations can be folded into one. Or perhaps eliminated entirely — sometimes abstraction adds no value at all.

This style of code review frequently covers code that wasn’t written by the person or team that initiated the code review. But that’s okay! It helps people get a bigger picture, and if the goal is to maintain overall system health, new code and existing shouldn’t be treated differently.

It’s also perfectly fine for the code review not to cover every new function. You’ll likely hit most of them while tracing the data (otherwise the functions would be dead code) but it’s better to emphasize the main flows. Once the code’s high-level structure is apparent, it’s usually clear which functions are more important than others.

After experimenting with various code review techniques, this approach has been the most effective and reliable over time. Make sure code reviews are somewhat frequent, however. After completion of every “project” or “story” or “module” or whatever, sit down for an hour with the code’s authors and appropriate tech leads and review the code. If the code review takes longer than an hour, people become too fatigued to add value.

Handling Code Review Follow-Up Tasks

At IMVU in particular, as we’re reviewing the code, someone will rapidly take notes into a shared document. The purpose of the review meeting is to review the code, not discuss the appropriate follow-up actions. It’s important not to interrupt the flow of the review meeting with a drawn-out discussion about what to do about one particular issue.

After the meeting, the team leads should categorize follow-up tasks into one of three categories:

  1. Do it right now. Usually tiny tweaks, for example: rename a function, call a different API, delete some commented-out code, move a function to a different file.
  2. Do it at the top of the next iteration. This is for small or medium-sized tasks that are worth doing. Examples: fix a bug, rework an API a bit, change an important but not-yet-ubiquitous file format.
  3. Would be nice someday. Delete these tasks. Don’t put them in a backlog. Maybe mention them to a research or infrastructure team. Example: “It would be great if our job scheduling system could specify dependencies declaratively.”

Nothing should float around on an amorphous backlog. If they are important, they’ll come up again. Plus, it’s very tempting to say “We’ll get to it” but you never will, and even if you have time, nobody will have context. So either get it done right away or be honest with yourself and consciously drop it.

Now go and review some code! 🙂

Tracing Leaks in Python: Find the Nearest Root

By Chad Austin

Garbage Collection Doesn’t Mean You Can Ignore Memory Altogether…

Garbage collection removes a great deal of burden from programming. In fact, garbage collection is a critical language feature for all languages where abstractions such as functional closures or coroutines are common, as they frequently create reference cycles.

IMVU is a mix of C++ and Python. The C++ code generally consists of small, cohesive objects with a clear ownership chain. An Avatar SceneObject owns a ModelInstance which owns a set of Meshes which own Materials which own Textures and so on… Since there are no cycles in this object graph, reference-counting with shared_ptr suffices.

The Python code, however, is full of messy object cycles. An asynchronous operation may hold a reference to a Room, while the Room may be holding a reference to the asynchronous operation. Often two related objects will be listening for events from the other. While Python’s garbage collector will happily take care of cycles, it’s still possible to leak objects.

Imagine these scenarios:

  • a leaked or living C++ object has a strong reference to a Python object.
  • a global cache has a reference to an instance’s bound method, which implicitly contains a reference to the instance.
  • two objects with __del__ methods participate in a cycle with each other, and Python refuses to decide which should destruct first

To detect these types of memory leaks, we use a LifeTimeMonitor utility:

a = SomeObject()
lm = LifeTimeMonitor(a)
del a
lm.assertDead() # succeeds

b = SomeObject()
lm = LifeTimeMonitor(b)
lm.assertDead() # raises ObjectNotDead

We use LifeTimeMonitor’s assertDead facility at key events, such as when a user closes a dialog box or 3D window. Take 3D windows as an example. Since they’re the root of an entire object subgraph, we would hate to inadvertently leak them. LifeTimeMonitor’s assertDead prevents us from introducing an object leak.

It’s good to know that an object leaked, but how can you determine why it can’t be collected?

Python’s Garbage Collection Algorithm

Let’s go over the basics of automatic garbage collection. In a garbage-collected system there are objects and objects can reference each other. Some objects are roots; that is, if an object is referenced by a root, it cannot be collected. Example roots are the stacks of live threads and the global module list. The graph formed by objects and their references is the object graph.

In SpiderMonkey, Mozilla’s JavaScript engine, the root set is explicitly-managed. SpiderMonkey’s GC traverses the object graph from the root set. If the GC does not reach an object, that object is destroyed. If C code creates a root object but fails to add it to the root set, it risks the GC deallocating the object while it’s still in use.

In Python however, the root set is implicit. All Python objects are ref-counted, and any that can refer to other objects — and potentially participate in an object cycle — are added to a global list upon construction. Each GC-tracked object can be queried for its referents. Python’s root set is implicit because anyone can create a root simply by incrementing an object’s refcount.

Since Python’s root set is implicit, its garbage collection algorithm differs slightly from SpiderMonkey’s. Python begins by setting GCRefs(o) to CurrentRefCount(o) for each GC-tracked PyObject o. Then it traverses all referents r of all GC-tracked PyObjects and subtracts 1 from GCRefs(r). Then, if GCRefs(o) is nonzero, o is an unknown reference, and thus a root. Python traverses the now-known root set and increments GCRefs(o) for any traversed objects. If any object o remains where GCRefs(o) == 0, that object is unreachable and thus collectible.

Finding a Path From the Nearest Root to the Leaked Object

Now that we know how Python’s garbage collector works, we can ask it for its set of roots by calculating GCRefs(o) for all objects o in gc.get_objects(). Then we perform a breadth-first-search from the root set to the leaked object. If the root set directly or indirectly refers to the leaked object, we return the path our search took.

Sounds simple, but there’s a catch! Imagine that the search function has signature:

PyObject* findPathToNearestRoot(PyObject* leakedObject);

leakedObject is a reference (incremented within Python’s function-call machinery itself) to the leaked object, making leakedObject a root!

To work around this, change findPathToNearestRoot so it accepts a singleton list containing a reference to the leaked object. findPathToNearestRoot can borrow that reference and clear the list, ensuring that leakedObject has no untracked references.

findPathToNearestRoot will find paths to expected Python roots like thread entry points and module objects. But, since it directly mirrors the behavior of Python’s GC, it will also find paths to leaked C references! Obviously, it can’t directly point you to the C code that leaked the reference, but the reference path should be enough of a clue to figure it out.

The Code

template<typename ArgType>
void traverse(PyObject* o, int (*visit)(PyObject* visitee, ArgType* arg), ArgType* arg) {
    if (Py_TYPE(o)->tp_traverse) {
        Py_TYPE(o)->tp_traverse(o, (visitproc)visit, arg);

typedef std::map<PyObject*, int> GCRefs;

static int subtractKnownReferences(PyObject* visitee, GCRefs* gcrefs) {
    if (gcrefs->count(visitee)) {
    return 0;

typedef int Backlink; // -1 = none

typedef std::vector< std::pair<Backlink, PyObject*> > ReferenceList;
struct Referents {
    std::set<PyObject*>& seen;
    Backlink backlink;
    ReferenceList& referenceList;

static int addReferents(PyObject* visitee, Referents* referents) {
    if (!referents->seen.count(visitee) && PyObject_IS_GC(visitee)) {
        referents->referenceList.push_back(std::make_pair(referents->backlink, visitee));
    return 0;

static Backlink findNextLevel(
    std::vector<PyObject*>& chain,
    const ReferenceList& roots,
    PyObject* goal,
    std::set<PyObject*>& seen
) {
    if (roots.empty()) {
        return -1;

    for (size_t i = 0; i < roots.size(); ++i) {
        if (roots[i].first != -1) {
            if (goal == roots[i].second) {
                return roots[i].first;

    ReferenceList nextLevel;
    for (size_t i = 0; i < roots.size(); ++i) {
        Referents referents = {seen, i, nextLevel};
        traverse(roots[i].second, &addReferents, &referents);

    Backlink backlink = findNextLevel(chain, nextLevel, goal, seen);
    if (backlink == -1) {
        return -1;

    return roots[backlink].first;

static std::vector<PyObject*> findReferenceChain(
    const std::vector<PyObject*>& roots,
    PyObject* goal
) {
    std::set<PyObject*> seen;
    ReferenceList unknownReferrer;
    for (size_t i = 0; i < roots.size(); ++i) {
        unknownReferrer.push_back(std::make_pair<Backlink>(-1, roots[i]));
    std::vector<PyObject*> rv;
    // going to return -1 no matter what: no backlink from roots
    findNextLevel(rv, unknownReferrer, goal, seen);
    return rv;

static object findPathToNearestRoot(const object& o) {
    if (!PyList_Check(o.ptr()) || PyList_GET_SIZE(o.ptr()) != 1) {
        PyErr_SetString(PyExc_TypeError, "findNearestRoot must take a list of length 1");

    // target = o.pop()
    object target(handle<>(borrowed(PyList_GET_ITEM(o.ptr(), 0))));
    if (-1 == PyList_SetSlice(o.ptr(), 0, 1, 0)) {

    object gc_module(handle<>(PyImport_ImportModule("gc")));
    object tracked_objects_list = gc_module.attr("get_objects")();
    // allocating the returned list may have run a GC, but tracked_objects won't be in the list

    std::vector<PyObject*> tracked_objects(len(tracked_objects_list));
    for (size_t i = 0; i < tracked_objects.size(); ++i) {
        object to = tracked_objects_list[i];
        tracked_objects[i] = to.ptr();
    tracked_objects_list = object();

    GCRefs gcrefs;

    // TODO: store allocation/gc count per generation

    for (size_t i = 0; i < tracked_objects.size(); ++i) {
        gcrefs[tracked_objects[i]] = tracked_objects[i]->ob_refcnt;

    for (size_t i = 0; i < tracked_objects.size(); ++i) {
        traverse(tracked_objects[i], subtractKnownReferences, &gcrefs);

    // BFS time

    std::vector<PyObject*> roots;
    for (GCRefs::const_iterator i = gcrefs.begin(); i != gcrefs.end(); ++i) {
        if (i->second && i->first != target.ptr()) { // Don't count the target as a root.
    std::vector<PyObject*> chain = findReferenceChain(roots, target.ptr());

    // TODO: assert that allocation/gc count per generation didn't change

    list rv;
    for (size_t i = 0; i < chain.size(); ++i) {

    return rv;

How to Embed Flash Into Your 3D Application

By Chad Austin

Writing user interfaces is hard. Writing usable interfaces is harder. Yet, the design of your interface is your product.

Products are living entities. They always want to grow, adapting to their users as users adapt to them. In that light, why build your user interface in a static technology like C++ or Java? It won’t be perfect the first time you build it, so prepare for change.

IMVU employs two technologies for rapidly iterating on and refining our client UIs: Flash and Gecko/HTML. Sure, integrating these technologies has a sizable up-front cost, but the iteration speed they provide easily pays for them.

Rapid iteration has some obvious benefits:

  1. reduces development cost
  2. reduces time to market

and some less-obvious benefits:

  1. better product/market fit: when you can change your UI, you will.
  2. improved product quality: little details distinguish mediocre products from great products. make changing details cheap and your Pinto will become a Cadillac.
  3. improved morale: both engineers and designers love watching their creations appear on the screen right before them. it’s why so many programmers create games!

I will show you how integrating Flash into a 3D application is easier than it sounds.

Should I use Adobe Flash or Scaleform GFx?

The two most common Flash implementations are Adobe’s ActiveX control (which has a 97% installed base!) and Scaleform GFx.

Adobe’s control has perfect compatibility with their tool chain (go figure!) but is closed-source and good luck getting help from Adobe.

Scaleform GFx is an alternate implementation of Flash designed to be embedded in 3D applications, but, last I checked, is not efficient on machines without GPUs. (Disclaimer: this information is two years old, so I encourage you to make your own evaluation.)

IMVU chose to embed Adobe’s player.

Deploying the Flash Runtime

Assuming you’re using Adobe’s Flash player, how will you deploy their runtime? Well, given Flash’s install base, you can get away with loading the Flash player already installed on the user’s computer. If they don’t have Flash, just require that they install it from your download page. Simple and easy.

Down the road, when Flash version incompatibilities and that last 5% of your possible market becomes important, you can request permission from Adobe to deploy the Flash player with your application.

Displaying SWFs

IMVU displays Flash in two contexts: traditional HWND windows and 2D overlays atop the 3D scene.

If you want to have something up and running in a day, buy f_in_box. Besides its awesome name, it’s cheap, comes with source code, and the support forums are fantastic. It’s a perfect way to bootstrap. After a weekend of playing with f_in_box, Dusty and I had a YouTube video playing in a texture on top of our 3D scene.

Once you run into f_in_box’s limitations, you can use the IShockwaveFlash and IOleInPlaceObjectWindowless COM interfaces directly. See Igor Makarav’s excellent tutorial and CFlashWnd class.

Rendering Flash as an HWND

For top-level UI elements use f_in_box or CFlashWnd directly. They’re perfectly suited for this. Seriously, it’s just a few lines of code. Look at their samples and go.

Rendering Flash as a 3D Overlay

Rendering Flash to a 3D window gets a bit tricky… Check out my next post!

It’s Hack Week at IMVU

By: James Birchler and Roland Blanton

Yesterday we kicked off another Hack Week at IMVU, a solid week when we put product development in the hands of IMVU engineers. What does this mean? An engineer can spend the week working on something they personally feel is valuable to the company. It’s a way to harness experience and insights from across the company and give everyone more ownership over what we are building here. The buzz in the building is tangible: there are fewer meetings, less process around group work, and people are focused on finishing their features to put them in front of customers.

Hack Week has been an integral part of our engineering culture since 2007, giving our software engineers a chance to guide product development and test their ideas. This tradition has resulted in many popular features like Outfits Management, Turbo Product Loading of 3D assets, IMVU Badges, and shopping directly from a 3D chat. All these features were driven by IMVU engineers during past Hack Weeks and then adopted by our product teams for release to all customers.

To help foster an environment of creativity, we use our A/B experiment system to make it easy and low-risk for us to test product innovations with customers. Rather than rely on the opinions in the room, we prefer getting feedback directly from customers to help guide our decisions.

In order to maximize chances for success, we follow some lightweight processes and rules:

  1. The goal in most cases is to deliver valuable features live to customers in experiments by the end of the week.
  2. Engineers choose projects to work on–sometimes from a team’s existing product backlog, and sometimes not.
  3. We work closely with product owners, user experience designers, technical leads, QA engineers, and other stakeholders to come up with what we think is a good plan.
  4. We start hacking, ultimately releasing features in A/B experiments to our customers.
  5. We only work on one project at a time (it’s pretty easy to find yourself starting many projects and never finishing, which runs counter to our overall goal of delivering value to customers).
  6. Everyone does a demo of their work at the end of Hack Week.

There is a lot of face to face, ad-hoc collaboration going on in the weeks preceding Hack Week, and during Hack Week itself.  The week concludes with demos to the entire company, a strong feeling of engagement with our customers and our product, and curiosity about what our customers will tell us about what we’ve built.