Trip Denton

Scenariographer: a tool for reverse engineering class usage scenarios from method invocation sequences

Typical documentation for object-oriented programs includes descriptions of the parameters and return types of each method in a class, but little or no information on valid method invocation sequences. Knowing the sequence with which methods of a class can be invoked is useful information especially for software engineers (e.g., developers, testers) who are actively involved in the maintenance of large software systems. This paper describes a new approach and a tool for generating class usage scenarios (i.e., how a class is used by other classes) from method invocations, which are collected during the execution of the software. Our approach is algorithmic and employs the notion of canonical sets to categorize method sequences into groups of similar sequences, where each group represents a usage scenario for a given class.

Selecting canonical views for view-based 3-D object recognition

Given a collection of sets of 2-D views of 3-D objects and a similarity measure between them, we present a method for summarizing the sets using a small subset called a bounded canonical set (BCS), whose members best represent the members of the original set. This means that members of the BCS are as dissimilar from each other as possible, while at the same time being as similar as possible to the nonBCS members. This paper would extend our earlier work on computing canonical sets [Denton, T, et al., June 2004] in several ways: by omitting the need for a multi-objective optimization, by allowing the imposition of cardinality constraints, and by introducing a total similarity function. We evaluate the applicability of BCS to view selection in a view-based object recognition environment.

Spectral and meta-heuristic algorithms for software clustering

When large software systems are reverse engineered, one of the views that is produced is the system decomposition hierarchy. This hierarchy shows the systems subsystems, the contents of the subsystems (i.e., modules or other subsystems), and so on. Software clustering tools create the system decomposition automatically or semi-automatically with the aid of the software engineer. The Bunch software clustering tool shows how meta-heuristic search algorithms can be applied to the software clustering problem, successfully. Unfortunately, we do not know how close the solutions produced by Bunch are to the optimal solution. We can only obtain the optimal solution for trivial systems using an exhaustive search. This paper presents evidence that Bunchs solutions are within a known factor of the optimal solution. We show this by applying spectral methods to the software clustering problem. The advantage of using spectral methods is that the results this technique produces are within a known factor of the optimal solution. Meta-heuristic search methods only guarantee local optimality, which may be far from the global optimum. In this paper, we apply the spectral methods to the software clustering problem and make comparisons to Bunch. We conducted a case study to draw our comparisons and to determine if an efficient clustering algorithm, one that guarantees a near-optimal solution, can be created.

On Computing the Canonical Features of Software Systems

Software applications typically have many features that vary in their similarity. We define a measurement of similarity between pairs of features based on their underlying implementations and use this measurement to compute a set of canonical features. The canonical features set (CFS) consists of a small number of features that are as dissimilar as possible to each other, yet are most representative of the features that are not in the CFS. The members of the CFS are distinguishing features and understanding their implementation provides the engineer with an overview of the system undergoing scrutiny. The members of the CFS can also be used as cluster centroids to partition the entire set of features. Partitioning the set of features can simplify the understanding of large and complex software systems. Additionally, when a specific feature must undergo maintenance, it is helpful to know which features are most closely related to it. We demonstrate the utility of our method through the analysis of the Jext, Firefox, and Gaim software systems

Reducing Program Comprehension Effort in Evolving Software by Recognizing Feature Implementation Convergence

The implementations of software features evolve as an application matures. We define a measure of feature implementation overlap that determines how similar features are in their execution by examining their call graphs. We consider how this measure changes over time, and evaluate the hypothesis that over time and subsequent versions of a software application, the implementations of semantically similar features converge. As the features of an application converge in their implementation, we are able to more effectively determine groups of semantically similar features and to reduce the cost of program comprehension by selecting few key features that give an overview of the system. We present a case study analyzing the features of the Jext, Firefox, and Gaim software systems to support our hypothesis.

Approximation of canonical sets and their applications to 2D view simplification

Given a set of patterns and a similarity measure between them, we would present an optimization framework to approximate a small subset, known as a canonical set, whose members closely resemble the members of the original set. We would present a combinatorial formulation of the canonical set problem in terms of quadratic optimization integer programming, present a relaxation through semidefinite programming, and propose a bounded performance rounding procedure for its approximation solution in polynomial time. Through a set of experiments we would investigate the application of canonical sets for computing a summary of views from a dense set of 2D views computed for a 3D object.

Stable bounded canonical sets and image matching

A common approach to the image matching problem is representing images as sets of features in some feature space followed by establishing correspondences among the features. Previous work by Huttenlocher and Ullman [1] shows how a similarity transformation – rotation, translation, and scaling – between two images may be determined assuming that three corresponding image points are known. While robust, such methods suffer from computational inefficiencies for general feature sets. We describe a method whereby the feature sets may be summarized using the stable bounded canonical set (SBCS), thus allowing the efficient computation of point correspondences between large feature sets. We use a notion of stability to influence the set summarization such that stable image features are preferred.

Combining different types of scale space interest points using canonical sets

Scale space interest points capture important photometric and deep structure information of an image. The information content of such points can be made explicit using image reconstruction. In this paper we will consider the problem of combining multiple types of interest points used for image reconstruction. It is shown that ordering the complete set of points by differential (quadratic) TV-norm (which works for single feature types) does not yield optimal results for combined point sets. The paper presents a method to solve this problem using canonical sets of scale space features. Qualitative and quantitative analysis show improved performance over simple ordering of points using the TV-norm.

Data Exchange for Unit-Cell Based Tissue Scaffold Design, Analysis and Fabrication

Tissue scaffolds must satisfy multiple design constraints, such as geometry, mechanical properties, and connectivity, to yield a functioning heterogeneous tissue. Onemethod that accounts for these multiple constraints is the unit-cellbased assembly approach. In this method, the volume that represents the natural tissue is filled with unit-cells that meet thedesign requirements of the volume. This approach requires data exchanges between several procedures including design, characterization, assembly, analysis, and fabrication procedures. In the paper, we present a data exchange system to store andretrieve the unit-cell information and customize data migrationamong applications. We also present our application of this dataexchange system to facilitate the management of data flow.

Multi - Parameter Optimization for Two-Phase Unit-Cell based Tissue Scaffolds

Porous three-dimensional (3D) tissue scaffolds directly influence cell attachment, proliferation, and guidance of new tissue formation. Cells respond to a scaffolds architecture, mechanical properties, and transport properties. Given the number of design constraints, scaffold design must include multiple design parameters. Using a unit-cell based assembly approach, we introduce a method to account for multiple design parameters during scaffold assembly. This paper presents our method for integrating multiple parameters for unit-cell selection.