July/August 2020 issue of acmqueue The July/August 2020 issue of acmqueue is out now

Subscribers and ACM Professional members login here


  Download PDF version of this article PDF

Software Development with Code Maps

Could those ubiquitous hand-drawn code diagrams become a thing of the past?

Robert DeLine, Gina Venolia, and Kael Rowan, Microsoft Research

Software developers regularly draw diagrams of their systems. To get a sense of how diagramming fits into a developer's daily work, consider this fictitious, but representative, story:

Jane is a developer who has been on her team so long that everyone calls her the team historian. Since the product just shipped a few weeks ago, Jane is finally getting around to some code cleanup she had planned for ages—namely, dropping a dependency on a library that is no longer supported. Jane uses her development environment to search for all the places where her product uses the unsupported library. She clicks through the results one by one and reads the code to understand how it uses the library. As she jumps around the code base, she sketches a class diagram on a notepad to capture the architectural dependencies she discovers.

Partway through this code-understanding task, there's a knock at the door. It's Joe, the newest member of the team. He is working on a bug and is confused about how one of the product's features is implemented. As the team historian, Jane is used to this type of question. They start the conversation by looking at an architectural diagram tacked to the wall near Jane's computer. To get into specifics, Jane draws a version of the diagram on the whiteboard, sketching only the relevant parts of the architecture but in more detail than the printed diagram. As she talks Joe through a use case, she overlays the diagram with arrows to show how different parts of the system interact. From time to time, she brings up relevant code in her development environment to relate the diagram back to the code.

After several minutes, Joe feels confident he understands the design and heads back to his office. Jane goes back to her own work. Between exploring the search results and answering Joe's questions, Jane's development environment now has dozens of open documents. Jane tries to resume her task but cannot find where she left off in all the clutter. She closes all open documents, reissues her original search, finds her place in the search results, and carries on exploring the dependency on the unsupported library.

This story illustrates the wide range of diagramming practice. The diagrams range in quality from sketches to high-quality posters; in formality, from idiosyncratic scribbles to well-defined notations; in longevity, from the duration of a single task to an entire release cycle; and in audience, from solo use, to anchoring a conversation, to communicating with the whole team or user community.

Although the practice illustrated in the story is widespread and useful, there are a few downsides where software could make an improvement. First, the diagrams are typically not tied to the code. To go from architecture-level to code-level discussions requires switching tools—for example, from the whiteboard to the development environment. This separation also causes poor task support. For example, Jane's code search and notepad diagram are not tied together in any way. The two can easily get out of sync and cannot be stored or retrieved together, as when Jane's diagram was available during task resumption but her search results were gone.

Second, there is no transition between team-level documentation and task- or conversation-specific diagrams. For example, Jane has to reproduce parts of the poster on the whiteboard in order to answer Joe's specific questions. Third, there is a lost opportunity to help deal with the disorientation Jane feels when confronting all the open documents on resuming her task.

Getting lost in a large code base is altogether too easy. The code consists of many thousands of symbols, with few visual landmarks to guide the eye. As a developer navigates the code, she follows hyperlinks, such as jumping from a method caller to a callee, with no visual transition to show where the jump landed. The more she navigates, the greater the pileup of document tabs. These navigation steps are not just for editing the code. Software developers also have a frequent need for information during their programming tasks.8,9 To try to find answers, they browse around the code and other documents, which adds both relevant and irrelevant documents to the environment's working set. With so much discontinuous navigation, a developer can easily become disoriented.

Better support for code diagrams in the development environment could support code understanding and communication, and could serve as a "map" to help keep developers oriented. The software visualization community has previously explored different types of maps, such as zoomable box-and-line diagrams10 and cityscapes,11 for tasks such as program understanding and analyzing project data. These maps are typically designed to supplement a standard development environment. Our goal is to integrate maps into the development environment such that developers can carry out most tasks within the map.

To address these issues, using a user-centered approach we are designing an interactive code map for development environments. In preparation for our initial design we conducted a series of field studies at Microsoft Corporation. We interviewed developers to find how and why they draw diagrams of their code, and we collected many example diagrams along the way.3 We also directly observed developers at work to watch their information-seeking behavior and to catalog their information needs.8 Finally, we did a participatory design of a paper-based code map to allow a development team to design its content and appearance and to witness how it supported their conversations.1 Using insights from these three studies, we are actively prototyping Code Canvas, a Microsoft Visual Studio plug-in that replaces the tabbed documents with a zoomable code map.5

How and Why Developers Diagram

To better understand how professional software developers use visual representations of their code, we interviewed nine developers at Microsoft to identify common scenarios, and then surveyed more than 400 developers to understand the scenarios more deeply.3 The three most frequently mentioned scenarios were:

Developers rated these three among the most important to their job functions. More than half of survey respondents indicated that diagrams were important in these scenarios. Most ad-hoc meetings were small, involving two or at most five people. While often done solo, understanding existing code and designing/refactoring involved pairs or small groups surprisingly often.

In a separate study we sought to understand developers' information needs while carrying out their development tasks.8 We observed 17 developers at Microsoft for about 90 minutes each, manually recorded their activity minute by minute, and coded these logs into 334 instances of information-seeking behavior. From this data, we identified 21 general information needs, clustered into seven work categories. Consistent with the previous study, we found that many of their information needs fell into the categories of understanding execution behavior and reasoning about design, summarized in table 1.

Table 1. Information Needs That Are Related to Diagramming Behavior

Understanding execution behavior Reasoning about design
  1. What code could have caused this behavior?
  2. What is statically related to this code?
  3. What code caused this program state?
  1. What is the purpose of this code?
  2. What is the program supposed to do?
  3. Why was this code implemented this way?
  4. What are the implications of this change?

In our observations, ad-hoc communication with coworkers was a common way of addressing a variety of information needs. Information needs most frequently addressed by talking with coworkers included the following:

  1. What have my coworkers been doing?
  2. What are the implications of this change?
  3. Is this problem worth fixing?
  4. What is the program supposed to do?
  5. In what situations does this failure occur
  6. How have resources I depend on changed?
  7. What code could have caused this behavior

This reliance on conversations with coworkers corresponds with the ad-hoc meeting scenario from the diagramming study.

From these two studies we know that developers have frequent, specific information needs when trying to understand existing code and planning code changes, and they often use diagrams when looking for answers. This suggests the plausible utility of a code map that answers these needs either directly or through interaction. We also know that developers often turn to coworkers to find the answers they need, and they create diagrams to supplement their conversations. This suggests that the code map should be shared among teammates so that they have a common spatial frame of reference.

Designing a Code Map

The question remains, what should the code map look like? We collected many examples of developers' visual representations of their code and identified the visual conventions they used.3 These ranged from sketches on whiteboards to diagrams carefully made using a drawing tool. We also looked at the visual conventions used by developers when representing code.2 Box-and-arrow diagrams were by far the most common representation, where each box represented some kind of software entity and each arrow indicated a relationship between two entities. Boxes were typically labeled, but arrows almost never were.

Some of these diagrams made casual use of visual notations, such as UML (Unified Modeling Language), but this was uncommon. Adjacency was sometimes used to indicate a relationship between two entities. Generally boxes were arranged so that relationships flowed in a more-or-less orderly direction, top to bottom or left to right. High-level groupings were indicated by surrounding boxes or curves, or by dividing lines. These visual conventions suggest a starting point for the design of a code map.

Armed with this general knowledge, we worked closely with a software development team called Oahu (a pseudonym) to develop a paper prototype of a code map.2 The Oahu team consisted of a few dozen people working on an incubation project of around 75,000 lines of C#. We first had each developer separately sketch the Oahu project on a large piece of paper. Four of these sketches are shown in Figure 1. Next we synthesized into a master drawing the common features and interesting exceptions that appeared in the sketches. For several weeks we repeated a daily cycle where we printed this drawing, hung it in the developers' offices, interviewed the team members for changes and reports of how they used it, and then revised the drawing based on their feedback. At their request, we incorporated types and method signatures reverse-engineered from their code, using a tool that we developed for the purpose.

Figure 1. Four Diagrams of the Oahu System

Through this process we arrived at a design (figure 2) that represented the code in a way that was meaningful to the team. The final design was basically an architectural layer diagram sprinkled with types (white boxes) containing method signatures. It closely followed the visual conventions we found in the earlier study. It also included some features that are not typical in architectural diagrams, such as representations of planned, but nonexistent code (for example, the empty white box beneath the mobile phones), colorized identifier fragments to aid visual searching (which we call concept keyword colorization), and the vertical banding representing concepts that cut across architectural layers.

Figure 2. The Oahu Code Map Designed through Team Participation

The callout shows the map at full resolution. The dashed rectangle is the map region shown in figure 3.

From these studies we learned that it is possible to design a code map from a simple set of visual conventions. The Oahu code map showed that a single map could represent an entire software project in a way that was meaningful to all the developers on the team. The team's response to the map was mixed. Two new hires on the Oahu team used the map extensively as part of their "onboarding" process, studying and annotating it often. Other team members had several criticisms, all stemming from the lack of interaction. They wanted to tailor the level of detail and the element positions to the needs of the discussion to change the content for the task (e.g., add call graphs to the map). We were able to address all these concerns in our Code Canvas, described next.

Maps at the Center of the Development Environment

We've incorporated insights from these studies into a prototype user interface for Microsoft Visual Studio, called Code Canvas, which makes a code map the central metaphor of the development experience.5 Rather than relying on tabbed documents and hierarchical overviews to navigate and edit the code, Code Canvas places all of a project's documents (code files, icons, user interface designs, etc.) onto a panning, zooming code map. The user can zoom out to get an overview of the project's structure and zoom in to view or edit code and other documents. (A video of Code Canvas is available here.)

We designed the look of the Code Canvas map based on our experience with the Oahu team. Figure 3 shows the Oahu project loaded into Code Canvas, in particular the upper left-hand corner of the UI layer (the area indicated with a dashed rectangle in figure 2). Using the visual conventions from the Oahu map, the Code Canvas map shows types as white boxes, with the identifiers labeled using concept keyword colorization, and with types organized into labeled bands (PopupMenu, Reminders, etc.). The type and concept boxes are positioned in Code Canvas in the same layout as the Oahu map.

Figure 3. The Code Canvas Version of the Oahu Map

The map is focused on the upper left corner of the UI layer and includes two overlays: search results in yellow and an execution trace as a series of red arrows. The callout shows the result of zooming into a method to edit its code.

An important lesson from the Oahu research is that developers assign meaning to the spatial layout of the code. Code Canvas therefore takes a mixed initiative approach to layout. The user is able to place any box on the map through direct manipulation, but Code Canvas also uses the MSAGL (Microsoft Automatic Graph Layout) engine to provide an initial layout for new code maps and to prevent occlusion and maintain relationships as the user makes subsequent changes to the layout.

Code Canvas uses a technique called semantic zoom to show different levels of detail at different levels of zoom. At the 10-percent level of zoom, the code itself is invisible because its size is less than a pixel per line, but the type names and member names are shown at a readable size. The callout in figure 3 shows the 100-percent level of zoom, where the code file itself is displayed using the standard editor, which provides the usual syntactic formatting and coloring and standard editor features such as code completion. At intermediate levels of zoom the code becomes visible, first in a skeletal form (in the style of Seesoft,6 a well-known software visualization tool from the early 1990s), then as readable text.

For a tour of Code Canvas's features, let's replay our initial story.

Jane's development environment shows an overview map of the whole project, called the HOME canvas. Its layout is as familiar to her as her hometown, since she has been moving around both of them for years. To start her task of understanding her project's dependency on the unsupported library, she searches for uses of the library. The search results are overlaid on the map in yellow boxes (as shown in figure 3) in addition to being listed in a separate window. She immediately sees the two parts of the code that depend on the library. She zooms into one of them to look more closely at exactly which classes are implicated and then clicks on an individual search result to look at the code itself.

After exploring this way for a while, she decides to focus on just the relevant code, so she creates a new "filtered canvas" in a new tab that contains the subset of the code containing the search results, maintaining the spatial relationships that help her stay oriented. As on the HOME canvas, the code on the filtered canvas is shown inside boxes representing the relevant classes and interfaces. In essence, this filtered canvas acts as the class diagram Jane previously drew on her notepad, except that, here, the search results and class diagram are automatically kept in sync and are persisted together.

Joe knocks on the door and asks Jane a question. She clicks over to the HOME canvas tab, zooms out, and points at parts of it to support what she's saying. The HOME canvas is shared among all team members, precisely to provide a common ground around which the team can have discussions. To explain the feature that is puzzling Joe, Jane sets a debugger breakpoint and runs the program. When the breakpoint is reached, Code Canvas shows the call stack using a series of red execution arrows, like those in figure 3. Jane then creates a second filtered canvas, focused on this call stack. She zooms out to give Joe a tour of the parts of the code involved in the feature. When Joe asks detailed questions about the algorithms, Jane zooms in on the relevant code. When the conversation with Joe is over, Jane simply closes the new tabs and returns to the one where she was working, which looks exactly as she left it.

In short, Code Canvas provides explicit task support through multiple canvases and uses stable, spatial layouts to keep users oriented. These design goals are shared by the Code Bubbles project at Brown University.1 Code Bubbles' strategy is to start with an empty canvas and add items as the user searches and browses the project. In contrast, Code Canvas starts with an overview and allows users to filter down to items of interest. In our future work, we will explore hybrids of the two approaches.


Based on the work practices we observed in our field studies, we believe making a code map central to the user interface of the development environment promises to reduce disorientation, answer common information needs, and anchor team conversations. Spatial memory and reasoning are little used by software developers today. In a lab-based evaluation of a previous version of our code-map design, we showed that developers form a reliable spatial memory of a code map during 90-minute sessions of programming tasks.4 By exploiting these cognitive resources, code maps will allow developers to be better grounded in the code, whether working solo or collaboratively. We believe that this will fundamentally change and improve the software development experience.


  1. Bragdon, A., Reiss, S. P., Zeleznik, R., Karumuri, S., Cheung, W., Kaplan, J., Coleman, C., Adeputra, F., LaViola Jr., J. J. 2010. Code Bubbles: rethinking the user interface paradigm of integrated development environments. In Proceedings of the 32nd International Conference on Software Engineering.
  2. Cherubini, M., Venolia, G., DeLine, R. 2007. Building an ecologically valid, large-scale diagram to help developers stay oriented in their code. In Proceedings of the IEEE Symposium on Visual Languages and Human-centric Computing (September).
  3. Cherubini, M., Venolia, G., DeLine, R., Ko, A. J. 2007. Let's go to the whiteboard: how and why software developers use drawings. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (May).
  4. DeLine, R., Czerwinski, M., Meyers, B., Venolia, G., Drucker, S., Robertson, G. 2006. Code thumbnails: using spatial memory to navigate source code. In Proceedings of the IEEE Symposium on Visual Languages and Human-centric Computing.
  5. DeLine, R., Rowan, K. 2010. Code Canvas: zooming towards better development environments. In Proceedings of the International Conference on Software Engineering (New Ideas and Emerging Results) (May).
  6. Eick, S. C., Steffen, J. L., Sumner Jr., E. E. 1992. Seesoft: a tool for visualizing line-oriented software statistics. IEEE Transactions on Software Engineering 18(11): 957-968.
  7. Kersten, M., Murphy, G. C. 2006. Using task context to improve programmer productivity. In Proceedings of the 14th ACM SIGSOFT International Symposium on Foundations of Software Engineering: 1–11.
  8. Ko, A. J., DeLine, R., Venolia, G. 2007. Information needs in collocated software development teams. In Proceedings of the 29th International Conference on Software Engineering (May).
  9. Sillito, J., Murphy, G. C., De Volder, K. 2008. Asking and answering questions during a programming change task. IEEE Transactions on Software Engineering.
  10. Storey, M. A., Best, C., Michaud, J., Rayside, D., Litoiu, M., Musen, M. 2002. SHriMP views: an interactive environment for information visualization and navigation. In Proceedings of the Conference on Human Factors in Computing Systems.
  11. Wettel, R., Lanza, M. 2007. Visualizing software systems as cities. In Proceedings of the IEEE International Workshop on Visualizing Software for Understanding and Analysis.


[email protected]

Robert DeLine (http://research.microsoft.com/~rdeline) is a principal researcher at Microsoft Research, working at the intersection of software engineering and human-computer interaction. His research group designs development tools in a user-centered fashion: they conduct studies of development teams to understand their work practice and prototype tools to improve that practice.

Gina Venolia (http://research.microsoft.com/~ginav) is a senior researcher with Microsoft Research in the Human Interactions of Programming group. Her research focuses on building systems that make knowledge flow more freely among people. She is studying distributed software teams and developing systems that exploit spatial memory to support navigation and team awareness.

Kael Rowan (http://research.microsoft.com/~kaelr) is a senior research software design engineer at Microsoft Research, focusing on the next generation of software development, including software visualization and spatial layout of source code. His background ranges from operating system internals and formal software verification to modern user interfaces and HCI.

© 2010 ACM 1542-7730/10/0700 $10.00


Originally published in Queue vol. 8, no. 7
see this item in the ACM Digital Library



David Crandall, Noah Snavely - Modeling People and Places with Internet Photo Collections
This article describes our work in using online photo collections to reconstruct information about the world and its inhabitants at both global and local scales. This work has been driven by the dramatic growth of social content-sharing Web sites, which have created immense online collections of user-generated visual data. Flickr.com alone currently hosts more than 6 billion images taken by more than 40 million unique users, while Facebook.com has said it grows by nearly 250 million photos every day.

Jeffrey Heer, Ben Shneiderman - Interactive Dynamics for Visual Analysis
The increasing scale and availability of digital data provides an extraordinary resource for informing public policy, scientific discovery, business strategy, and even our personal lives. To get the most out of such data, however, users must be able to make sense of it: to pursue questions, uncover patterns of interest, and identify (and potentially correct) errors. In concert with data-management systems and statistical algorithms, analysis requires contextualized human judgments regarding the domain-specific significance of the clusters, trends, and outliers discovered in data.

Brendan Gregg - Visualizing System Latency
When I/O latency is presented as a visual heat map, some intriguing and beautiful patterns can emerge. These patterns provide insight into how a system is actually performing and what kinds of latency end-user applications experience. Many characteristics seen in these patterns are still not understood, but so far their analysis is revealing systemic behaviors that were previously unknown.

Jeffrey Heer, Michael Bostock, Vadim Ogievetsky - A Tour through the Visualization Zoo
Thanks to advances in sensing, networking, and data management, our society is producing digital information at an astonishing rate. According to one estimate, in 2010 alone we will generate 1,200 exabytes -- 60 million times the content of the Library of Congress. Within this deluge of data lies a wealth of valuable information on how we conduct our businesses, governments, and personal lives. To put the information to good use, we must find ways to explore, relate, and communicate the data meaningfully.

© 2020 ACM, Inc. All Rights Reserved.