Trace Abstraction Framework and Techniques

Understanding the behavioural aspects of software systems can help in a variety of software engineering tasks such as debugging, feature enhancement, performance analysis, and security. Software behaviour is typically represented in the form of execution traces. Traces, however, have historically been difficult to analyze due to the overwhelming size of typical traces. Trace analysis, more particularly trace abstraction and simplification, techniques have emerged to overcome the challenges of working with large traces. Existing traces analysis tools rely on some sort of visualization techniques to help software engineers make sense of trace content. Many of these techniques have been studied and found to be limited in many ways. In this thesis, we present a novel approach for trace analysis inspired by the way the human brain and perception systems operate. The idea is to mimic the psychological processes that have been developed over the years to explain how our perception system deals with huge volume of visual data. We show how similar mechanisms can be applied to the abstraction and simplification of large traces. As part of this framework, we present a novel trace analysis technique that automatically divides the content of a large trace, generated from execution of a target system, into meaningful segments that correspond to the system’s main execution phases such as initializing variables, performing a specific computation, etc. We also propose a trace sampling technique that not only reduces the size of a trace but also results in a sampled trace that is representative of the original trace by ensuring that the desired characteristics of an execution are distributed similarly in both the sampled and the original trace. Our approach is based on stratified sampling and uses the concept of execution phases as strata. Finally, we propose an approach to automatically identify the most relevant trace components of each execution phases. This approach also enables an efficient representation of the flow of phases by detecting redundant phases using a cosine similarity metric. The techniques presented in this thesis have been validated by applying to a variety of target systems. The obtained results demonstrate the effectiveness and usefulness of our methods

Regression test selection for distributed Java RMI programs by means of formal concept analysis

Software maintenance is the process of modifying an existing system to ensure that it meets current and future requirements. As a result, performing regression testing becomes an essential but time consuming aspect of any maintenance activity. Regression testing is initiated after a programmer has made changes to a program that may have inadvertently introduced errors. It is a quality control approach to ensure that the newly modified code still complies with its specified requirements and that unmodified code has not been affected by the maintenance activity. In the literature various types of test selection techniques have been proposed to reduce the effort associated with re-executing the required test cases. However, the majority of these approach has been focusing only on sequential programs, and provide no or only very limited support for distributed programs or database-driven applications. The thesis presents a lightweight methodology, which applies Formal Concept Analysis to support a regression test selection analysis, in combination with execution trace collection and external data sharing analysis, for distributed Java RMI programs. Two Eclipse plug-ins were developed to automate the regression test selection process and to evaluate our methodology

Conceptual roles of data in program: analyses and applications

Program comprehension is the prerequisite for many software evolution and maintenance tasks. Currently, the research falls short in addressing how to build tools that can use domain-specific knowledge to provide powerful capabilities for extracting valuable information for facilitating program comprehension. Such capabilities are critical for working with large and complex program where program comprehension often is not possible without the help of domain-specific knowledge.;Our research advances the state-of-art in program analysis techniques based on domain-specific knowledge. The program artifacts including variables and methods are carriers of domain concepts that provide the key to understand programs. Our program analysis is directed by domain knowledge stored as domain-specific rules. Our analysis is iterative and interactive. It is based on flexible inference rules and inter-exchangeable and extensible information storage. We designed and developed a comprehensive software environment SeeCORE based on our knowledge-centric analysis methodology. The SeeCORE tool provides multiple views and abstractions to assist in understanding complex programs. The case studies demonstrate the effectiveness of our method. We demonstrate the flexibility of our approach by analyzing two legacy programs in distinct domains

Structural usability techniques for dependable HCI.

Since their invention in the middle of the twentieth century, interactive computerised systems have become more and more common to the point of ubiquity. While formal techniques have developed as tools for understanding and proving things about the behaviour of computerised systems, those that involve interaction with human users present some particular challenges which are less well addressed by traditional formal methods. There is an under-explored space where interaction and the high assurances provided by formal approaches meet. This thesis presents two techniques which fit into this space, and which can be used to automatically build and analyse formal models of the interaction behaviour of existing systems. Model discovery is a technique for building a state space-based formal model of the interaction behaviour of a running system. The approach systematically and exhaustively simulates the actions of a user of the system; this is a dynamic analysis technique which requires tight integration with the running system and (in practice) its codebase but which, when set up, can proceed entirely automatically. Theorem discovery is a technique for analysing a state space-based formal model of the interaction behaviour of a system, looking for strings of user actions that have equivalent effects across all states of the system. The approach systematically computes and compares the effects of ever-longer strings of actions, though insights can also arise from strings that are almost equivalent, and also from considering the meaning of sets of such equivalences. The thesis introduces and exemplifies each technique, considers how they may be used together, and demonstrates their utility and novelty, with case studies

Analytic Provenance for Software Reverse Engineers

Reverse engineering is a time-consuming process essential to software-security tasks such as malware analysis and vulnerability discovery. During the process, an engineer will follow multiple leads to determine how the software functions. The combination of time and possible explanations makes it difficult for the engineers to maintain a context of their findings within the overall task. Analytic provenance tools have demonstrated value in similarly complex fields that require open-ended exploration and hypothesis vetting. However, they have not been explored in the reverse engineering domain. This dissertation presents SensorRE, the first analytic provenance tool designed to support software reverse engineers. A semi-structured interview with experts led to the design and implementation of the system. We describe the visual interfaces and their integration within an existing software analysis tool. SensorRE automatically captures user\u27s sense making actions and provides a graph and storyboard view to support further analysis. User study results with both experts and graduate students demonstrate that SensorRE is easy to use and that it improved the participants\u27 exploration process