University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

One of the most essential parts of any software requirements analysis effort is the exploration of alternative ways by which stakeholder problems can be solved. Systematic modeling and analysis of requirements variability allows better decision making during the early requirements phase and substantiates design choices pertaining to the configurability aspect of the system-to-be. This thesis proposes the use of goal models for capturing and reasoning about requirements variability. The goal models we adopt consist of AND/OR decompositions of stakeholder goals and express alternative ways by which stakeholders may wish to achieve them. By capturing goal variability using such models, we propose a shift of focus from variability of the software design, to variability of the problem that the design is intended to solve. This way, we ensure that every important variation of the problem is identified and analyzed before variations of the solution are specified. The thesis exploits opportunities that arise from this new viewpoint. Firstly, a variability-intensive goal decomposition process is proposed. The process is based on associating each high-level goal to a set of variability concerns that must be addressed through decomposition. We introduce a universal categorization of such concerns and also show how domain-specific variability concerns can be identified by annotating domain corpora. Concern-driven decomposition offers a structured way of thinking about problem variability, while systematizing its identification process. Further, an expressive LTL-based preference language is introduced to support leverage of large spaces of goal alternatives. The language allows the expression of preferences over behavioral and qualitative properties of solutions and a reasoning tool allows the identification of alternatives that satisfy these preferences. This way, individual stakeholders can get the solution that exactly fits their needs in a particular situation, through simply specifying desired high-level characteristics of these solutions. Finally, a framework for connecting alternatives at the goal level to alternative configurations of common desktop applications is presented. The framework shows how a vast number of configurations of a software application can be evaluated and ranked with respect to a small number of quality goals that are more intuitive to and comprehensible by end users.

A telecommunication feature is an optional or incremental unit of functionality, such as call display (CD) and call forwarding (CF). A feature interaction occurs when, in the presence of other features, the actual behavior of a feature becomes inconsistent with its specified behavior. This feature interaction problem is a long-existing problem in telephony, and it becomes an increasingly pressing problem as more and more sophisticated features are developed and put into use. It takes a lot of effort to test that the addition of a new feature to a system doesn’t affect any existing features in an undesired way. Distributed Feature Composition (DFC) proposed by Michael Jackson and Pamela Zave, is an architectural approach to the feature interaction problem. Telecommunication features are modeled as independent components, which we call boxes. Boxes are composed in a pipe-and-filter-like sequence to form an end-to-end call. Our work studies the behaviour of single feature boxes. We translate BoxTalk specifications into another format, that is more conducive to automated reasoning. We build formal models on the translated format, then the formal models are checked by a model checker, SPIN, against DFC compliance properties written in Linear Temporal Logic (LTL). From BoxTalk specifications to Promela models, the translation takes steps: 1) Explicating BoxTalk, which expands BoxTalk macros and presents its implicit behaviours as explicit transitions. 2) Define BoxTalk semantics in terms of Template Semantics. 3) Construct Promela model from Template Semantics HTS. Our case studies exercised this translation process, and the resulting models are proven to hold desired properties.

Success for many businesses depends on their information software systems. Keeping these systems operational is critical, as failure in these systems is costly. Such systems are in many cases sophisticated, distributed and dynamically composed. To ensure high availability and correct operation, it is essential that failures be detected promptly, their causes diagnosed and remedial actions taken. Although automated recovery approaches exists for specific problem domains, the problem-resolution process is in many cases manual and painstaking. Computer support personnel put a great deal of effort into resolving the reported failures. The growing size and complexity of these systems creates the need to automate this process. The primary focus of our research is on automated fault diagnosis and recovery using discrete monitoring data such as log files and notifications. Our goal is to quickly pinpoint the root-cause of a failure. Our contributions are: Modelling discrete monitoring data for automated analysis, automatically leveraging common symptoms of failures from historic monitoring data using such models to pinpoint faults, and providing a model for decision-making under uncertainty such that appropriate recovery actions are chosen. Failures in such systems are caused by software defects, human error, hardware failures, environmental conditions and malicious behaviour. Our primary focus in this thesis is on software defects and misconfiguration.

Thesis (Ph.D.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 106-116)

Thesis [M.H. Miklaski]-(M.S. in Systems Technology) and (M.S. in Software Engineering)--Naval Postgraduate School, December 2003. Thesis [J.D. Babbitt]-(M.S. in Computer Science)--Naval Postgraduate School, December 2003. / Thesis advisor(s): Man-Tak Shing, James Bret Michael. Includes bibliographical references (p. 287-289). Also available online.

Latent topic models are statistical structures in which a "latent topic" describes some relationship between parts of the data. Co-maintenance is defined as an observable property of software systems under source control in which source code fragments are modified together in some time frame. When topic models are applied to software systems, latent topics emerge from code fragments. However, it is not yet known what these latent topics mean. In this research, we analyse software maintenance history, and show that latent topics often correspond to code fragments that are maintained together. Moreover, we show that latent topic models can identify such co-maintenance relationships even with no supervision. We can use this correlation both to categorize and understand maintenance history, and to predict future co-maintenance in practice. The relationship between co-maintenance and topics is directly analysed within changelists, with respect to both local pairwise code fragment similarity and global system-wide fragment similarity. This analysis is used to evaluate topic models used with a domain-specific programming language for web service similarity detection, and to estimate appropriate topic counts for modelling source code. / Thesis (Ph.D, Computing) -- Queen's University, 2012-04-30 18:16:04.05

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