A comparative evaluation of dynamic visualisation tools

Despite their potential applications in software comprehension, it appears that dynamic visualisation tools are seldom used outside the research laboratory. This paper presents an empirical evaluation of five dynamic visualisation tools - AVID, Jinsight, jRMTool, Together ControlCenter diagrams and Together ControlCenter debugger. The tools were evaluated on a number of general software comprehension and specific reverse engineering tasks using the HotDraw objectoriented framework. The tasks considered typical comprehension issues, including identification of software structure and behaviour, design pattern extraction, extensibility potential, maintenance issues, functionality location, and runtime load. The results revealed that the level of abstraction employed by a tool affects its success in different tasks, and that tools were more successful in addressing specific reverse engineering tasks than general software comprehension activities. It was found that no one tool performs well in all tasks, and some tasks were beyond the capabilities of all five tools. This paper concludes with suggestions for improving the efficacy of such tools

Software visualisation for object-oriented program comprehension

Software visualisation is the process of modelling software systems for comprehension. The comprehension of software systems both during and after development is a crucial component of the software process. The complex interactions inherent in the object-oriented paradigm make visualisation a particularly appropriate comprehension technique, and the large volume of information typically generated during visualisation necessitates tool support

Supporting Project Comprehension with Revision Control System Repository Analysis

Context: Project comprehension is an activity relevant to all aspects of software engineering, from requirements specification to maintenance. The historical, transactional data stored in revision control systems can be mined and analysed to produce a great deal of information about a project. Aims: This research aims to explore how the data-mining, analysis and presentation of revision control systems can be used to augment aspects of project comprehension, including change prediction, maintenance, visualization, management, profiling, sampling and assessment. Method: A series of case studies investigate how transactional data can be used to support project comprehension. A thematic analysis of revision logs is used to explore the development process and developer behaviour. A benchmarking study of a history-based model of change prediction is conducted to assess how successfully such a technique can be used to augment syntax-based models. A visualization tool is developed for managers of student projects with the aim of evaluating what visualizations best support their roles. Finally, a quasi-experiment is conducted to determine how well an algorithmic model can automatically select a representative sample of code entities from a project, in comparison with expert strategies. Results: The thematic analysis case study classified maintenance activities in 22 undergraduate projects and four real-world projects. The change prediction study calculated information retrieval metrics for 34 undergraduate projects and three real-world projects, as well as an in-depth exploration of the model's performance and applications in two selected projects. File samples for seven projects were generated by six experts and three heuristic models and compared to assess agreement rates, both within the experts and between the experts and the models. Conclusions: When the results from each study are evaluated together, the evidence strongly shows that the information stored in revision control systems can indeed be used to support a range of project comprehension activities in a manner which complements existing, syntax-based techniques. The case studies also help to develop the empirical foundation of repository analysis in the areas of visualization, maintenance, sampling, profiling and management; the research also shows that students can be viable substitutes for industrial practitioners in certain areas of software engineering research, which weakens one of the primary obstacles to empirical studies in these areas

A Study on Software Testability and the Quality of Testing in Object-Oriented Systems

Software testing is known to be important to the delivery of high-quality systems, but it is also challenging, expensive and time-consuming. This has motivated academic and industrial researchers to seek ways to improve the testability of software. Software testability is the ease with which a software artefact can be effectively tested. The first step towards building testable software components is to understand the factors – of software processes, products and people – that are related to and can influence software testability. In particular, the goal of this thesis is to provide researchers and practitioners with a comprehensive understanding of design and source code factors that can affect the testability of a class in object oriented systems. This thesis considers three different views on software testability that address three related aspects: 1) the distribution of unit tests in relation to the dynamic coupling and centrality of software production classes, 2) the relationship between dynamic (i.e., runtime) software properties and class testability, and 3) the relationship between code smells, test smells and the factors related to smells distribution. The thesis utilises a combination of source code analysis techniques (both static and dynamic), software metrics, software visualisation techniques and graph-based metrics (from complex networks theory) to address its goals and objectives. A systematic mapping study was first conducted to thoroughly investigate the body of research on dynamic software metrics and to identify issues associated with their selection, design and implementation. This mapping study identified, evaluated and classified 62 research works based on a pre-tested protocol and a set of classification criteria. Based on the findings of this study, a number of dynamic metrics were selected and used in the experiments that were then conducted. The thesis demonstrates that by using a combination of visualisation, dynamic analysis, static analysis and graph-based metrics it is feasible to identify central classes and to diagrammatically depict testing coverage information. Experimental results show that, even in projects with high test coverage, some classes appear to be left without any direct unit testing, even though they play a central role during a typical execution profile. It is contended that the proposed visualisation techniques could be particularly helpful when developers need to maintain and reengineer existing test suites. Another important finding of this thesis is that frequently executed and tightly coupled classes are correlated with the testability of the class – such classes require larger unit tests and more test cases. This information could inform estimates of the effort required to test classes when developing new unit tests or when maintaining and refactoring existing tests. An additional key finding of this thesis is that test and code smells, in general, can have a negative impact on class testability. Increasing levels of size and complexity in code are associated with the increased presence of test smells. In addition, production classes that contain smells generally require larger unit tests, and are also likely to be associated with test smells in their associated unit tests. There are some particular smells that are more significantly associated with class testability than other smells. Furthermore, some particular code smells can be seen as a sign for the presence of test smells, as some test and code smells are found to co-occur in the test and production code. These results suggest that code smells, and specifically certain types of smells, as well as measures of size and complexity, can be used to provide a more comprehensive indication of smells likely to emerge in test code produced subsequently (or vice versa in a test-first context). Such findings should contribute positively to the work of testers and maintainers when writing unit tests and when refactoring and maintaining existing tests

CoMoVA - A comprehension measurement framework for visualization systems

Despite the burgeoning interest shown in visualizations by many disciplines, there yet remains the unresolved question concerning comprehension. Is the concept that is being communicated through the visual easily grasped and clearly interpreted? Visual comprehension is that characteristic of any visualization system, which deals with how efficiently and effectively users are able to grasp the underlying concepts through suitable interactions provided for exploring the visually represented information. Comprehension has been considered a very complex subject, which is intangible and subjective in nature. Assessment of comprehension can help to determine the true usefulness of visualization systems to the intended users. A principal contribution of this research is the formulation of an empirical evaluation framework for systematically assessing comprehension support provided by a visualization system to its intended users. To assess comprehension i.e. to measure this seemingly immeasurable factor of visualization systems, we propose a set of criteria based on a detailed analysis of information flow from the raw data to the cognition of information in human mind. Our comprehension criteria are adapted from the pioneering work of two eminent researchers - Donald A. Norman and Aaron Marcus, who have investigated the issues of human perception and cognition, and visual effectiveness respectively. The proposed criteria have been refined with the help of opinions from experts. To gauge and verify the efficacy of these criteria in a practical sense, they were then applied to a bioinformatics visualization study tool and an immersive art visualization environment. Given the vast variety of users and their visualization goals, it may be noted that it is difficult for one to decide on the effectiveness of different visualization tools/techniques in a context independent fashion. We therefore propose an innovative way of evaluating a visualization technique by encapsulating it in a visualization pattern where it is seen as a solution to the visualization problem in a specific context. These visualization patterns guide the tool users/evaluators to compare, understand and select appropriate visualization tools/techniques. Lastly, we propose a novel framework named as CoMoVA (Comprehension Model for Visualization Assessment) that incorporates 'context of use', visualization patterns, visual design principles and important cognitive principles into a coherent whole that can be used to effectively tell us in a more quantifiable manner the benefits of visual representations and interactions provided by a system to the intended audience. Our approach of evaluation of visualization systems is similar to other questionnaire-based approaches such as SUMI (Software Usability Measurement Inventory), where all the questions deal with the measurement of a common trait. We apply this framework to two static software visualization tools in the software visualization domain to demonstrate the practical benefits of using such a framework