Stephen G. Eick

Seesoft-a tool for visualizing line oriented software statistics

The Seesoft software visualization system allows one to analyze up to 50000 lines of code simultaneously by mapping each line of code into a thin row. The color of each row indicates a statistic of interest, e.g., red rows are those most recently changed, and blue are those least recently changed. Seesoft displays data derived from a variety of sources, such as version control systems that track the age, programmer, and purpose of the code (e.g., control ISDN lamps, fix bug in call forwarding); static analyses, (e.g., locations where functions are called); and dynamic analyses (e.g., profiling). By means of direct manipulation and high interaction graphics, the user can manipulate this reduced representation of the code in order to find interesting patterns. Further insight is obtained by using additional windows to display the actual code. Potential applications for Seesoft include discovery, project management, code tuning, and analysis of development methodologies. >

Does code decay? Assessing the evidence from change management data

A central feature of the evolution of large software systems is that change-which is necessary to add new functionality, accommodate new hardware, and repair faults-becomes increasingly difficult over time. We approach this phenomenon, which we term code decay, scientifically and statistically. We define code decay and propose a number of measurements (code decay indices) on software and on the organizations that produce it, that serve as symptoms, risk factors, and predictors of decay. Using an unusually rich data set (the fifteen-plus year change history of the millions of lines of software for a telephone switching system), we find mixed, but on the whole persuasive, statistical evidence of code decay, which is corroborated by developers of the code. Suggestive indications that perfective maintenance can retard code decay are also discussed.

Software visualization in the large

The invisible nature of software hides system complexity, particularly for large team-oriented projects. The authors have evolved four innovative visual representations of code to help solve this problem: line representation; pixel representation; file summary representation; and hierarchical representation. We first describe these four visual code representations and then discuss the interaction techniques for manipulating them. We illustrate our software visualization techniques through five case studies. The first three focus on software history and static software characteristics; the last two discuss execution behavior. The software library and its implementation are then described. Finally, we briefly review some related work and compare and contrast our different techniques for visualizing software.

Visualizing network data

Networks are critical to modern society, and a thorough understanding of how they behave is crucial to their efficient operation. Fortunately, data on networks is plentiful; by visualizing this data, it is possible to greatly improve our understanding. Our focus is on visualizing the data associated with a network and not on simply visualizing the structure of the network itself. We begin with three static network displays; two of these use geographical relationships, while the third is a matrix arrangement that gives equal emphasis to all network links. Static displays can be swamped with large amounts of data; hence we introduce direct manipulation techniques that permit the graphs to continue to reveal relationships in the context of much more data. In effect, the static displays are parameterized so that interesting views may easily be discovered interactively. The software to carry out this network visualization is called SeeNet. >

The physics of the M t /G/ ∞ symbol Queue

We establish some general structural results and derive some simple formulas describing the time-dependent performance of the Mt/G/∞ queue (with a nonhomogeneous Poisson arrival process). We know that, for appropriate initial conditions, the number of busy servers at time t has a Poisson distribution for each t. Our results show how the time-dependent mean function m depends on the time-dependent arrival-rate function λ and the service-time distribution. For example, when λ is quadratic, the mean m(t) coincides with the pointwise stationary approximation λ(t)E[S], where S is a service time, except for a time lag and a space shift. It is significant that the well known insensitivity property of the stationary M/G/∞ model does not hold for the nonstationary Mt/G/∞ model; the time-dependent mean function m depends on the service-time distribution beyond its mean. The service-time stationary-excess distribution plays an important role. When λ is decreasing before time t, m(t) is increasing in the service-time...

Navigating large networks with hierarchies

This paper is aimed at the exploratory visualization of networks where there is a strength or weight associated with each link, and makes use of any hierarchy present on the nodes to aid the investigation of large networks. It describes a method of placing nodes on the plane that gives meaning to their relative positions. The paper discusses how linking and interaction principles aid the user in the exploration. Two examples are given; one of electronic mail communication over eight months within a department, another concerned with changes to a large section of a computer program.<<ETX>>

Visualizing software changes

A key problem in software engineering is changing the code. We present a sequence of visualizations and visual metaphors designed to help engineers understand and manage the software change process. The principal metaphors are matrix views, cityscapes, bar and pie charts, data sheets and networks. Linked by selection mechanisms, multiple views are combined to form perspectives that both enable discovery of high-level structure in software change data and allow effective access to details of those data. Use of the views and perspectives is illustrated in two important contexts: understanding software change by exploration of software change data and management of software development. Our approach complements existing visualizations of software structure and software execution.

Data visualization sliders

Computer sliders are a generic user input mechanism for specifying a numeric value from a range. For data visualization, the effectiveness of sliders may be increased by using the space inside the slider as • an interactive color scale, • a barplot for discrete data, and • a density plot for continuous data. The idea is to show the selected values in relation to the data and its distribution. Furthermore, the selection mechanism may be generalized using a painting metaphor to specify arbitrary, disconnected intervals while maintaining an intuitive user-interface.

Information rich glyphs for software management data

Three high-dimensional glyphs for viewing software project management data assist in perhaps the most challenging engineering task in modern times managing large software projects. In data exploration, glyphs refer to graphical objects or symbols that represent data through visual parameters that are either spatial (positions x or y), retinal (color and size), or temporal. Common examples of graphical objects include the bars in a bar chart or the points within a scatter plot. We focus on glyphs for visualizing software project management data. Any large-scale project will have many different classes of resources (lab equipment, staff time, machine cycles, disk resources, interim deliverables, and customer commitments) that must be scheduled and tracked. Inevitably, problems will arise and solutions must be found. To support the management process, information systems collect and maintain large status databases. We aim to support and improve the understanding of this information through visualization. Our glyphs are designed to expose patterns among sets of software artifacts and to help identify differences between items.

3D geographic network displays

Many types of information may be represented as graphs or networks with the nodes corresponding to entities and the links to relationships between entities. Often there is geographical information associated with the network. The traditional way to visualize geographical networks employs node and link displays on a two-dimensional map. These displays are easily overwhelmed, and for large networks become visually cluttered and confusing. To overcome these problems we have invented five novel network views that generalize the traditional displays. Two of the views show the complete network, while the other three concentrate on a portion of a larger network defined by connectivity to a given node. Our new visual metaphors retain many of the well-known advantages of the traditional network maps, while exploiting three-dimensional graphics to address some of the fundamental problems limiting the scalability of two-dimensional displays.