Understanding the implementation of a certain feature of a system requires to identify the computational units of the system that contribute to this feature. In many cases, the mapping of features to the source code is poorly documented. In this paper, we present a semi-automatic technique that reconstructs the mapping for features that are triggered by the user and exhibit an observable behavior. The mapping is in general not injective; that is, a computational unit may contribute to several features. Our technique allows to distinguish between general and specific computational units with respect to a given set of features. For a set of features, it also identifies jointly and distinctly required computational units.

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Concept location identifies parts of a software system that implement a specific concept that originates from the problem or the solution domain. Concept location is a very common software engineering activity that directly supports software maintenance and evolution tasks such as incremental change and reverse engineering. This paper addresses the problem of concept location using an advanced information retrieval method, Latent Semantic Indexing (LSI). LSI is used to map concepts expressed in natural language by the programmer to the relevant parts of the source code. Results of a case study on NCSA Mosaic are presented and compared with previously published results of other static methods for concept location. 1

The issues of code scattering and tangling, thus of achieving a better modularity for a system’s concerns, are addressed by the paradigm of aspect orientation. Aspect mining is a reverse engineering process that aims at finding crosscutting concerns in existing systems. This paper describes a technique based on determining methods that are called from many different places (and hence have a high fan-in) to identify candidate aspects in a number of open-source Java systems. The most interesting aspects identified are discussed in detail, which includes several concerns not previously discussed in the aspect-oriented literature. The results show that a significant number of aspects can be recognized using fan-in analysis, and that the technique is suitable for a high degree of automation. 1.

Much of software developers’ time is spent understanding unfamiliar code. To better understand how developers gain this understanding and how software development environments might be involved, a study was performed in which developers were given an unfamiliar program and asked to work on two debugging tasks and three enhancement tasks for 70 minutes. The study found that developers interleaved three activities. They began by searching for relevant code both manually and using search tools; however, they based their searches on limited and misrepresentative cues in the code, environment, and executing program, often leading to failed searches. When developers found relevant code, they followed its incoming and outgoing dependencies, often returning to it and navigating its other dependencies; while doing so, however, Eclipse’s navigational tools caused significant overhead. Developers collected code and other information that they believed would be necessary to edit, duplicate, or otherwise refer to later by encoding it in the interactive state of Eclipse’s package explorer, file tabs, and scroll bars. However, developers lost track of relevant code as these interfaces were used for other tasks, and developers were forced to find it again. These issues caused developers to spend, on average, 35 percent of their time performing the mechanics of navigation within and between source files. These observations suggest a new model of program understanding grounded in theories of information foraging and suggest ideas for tools that help developers seek, relate, and collect information in a more effective and explicit manner.

To facilitate software maintenance and evolution, a helpful step is to locate features concerned in a particular maintenance task. In the literature, both dynamic and interactive approaches have been proposed for feature location. In this paper, we present a static and non-interactive method for achieving this objective. The main idea of our approach is to use the information retrieval (IR) technology to reveal the basic connections between features and computational units in source code. Due to the characteristics of the retrieved connections, we use a static representation of the source code named BRCG to further recover both the relevant and the specific computational units for each feature. Furthermore, we recover the relationships among the relevant units for each feature. A premise of our approach is that programmers should use meaningful names as identifiers. We perform an experimental study based on a GNU system to evaluate our approach. In the experimental study, we present the detailed quantitative experimental data and give the qualitative analytical results. 1.

Many program evolution tasks involve source code that is not modularized as a single unit. Furthermore, the source code relevant to a change task often implements different concerns, or high-level concepts that a developer must consider. Finding and understanding concerns scattered in source code is a difficult task that accounts for a large proportion of the effort of performing program evolution. One possibility to mitigate this problem is to produce textual documentation that describes scattered concerns. However, this approach is impractical because it is costly, and because, as a program evolves, the documentation becomes inconsistent with the source code. The thesis of this dissertation is that a description of concerns, representing program structures and linked to source code, that can be produced cost-effectively during program investigation activities, can help developers perform software evolution tasks more systematically, and on different versions of a system. To validate the claims of this thesis, we have developed a model for a structure, called concern graph, that describes concerns in source code in terms of relations between program elements. The model also defines precisely the notion of inconsistency between a concern graph and the

Developers working on existing programs repeatedly have to address concerns, or aspects, that are not well mod-ularized in the source code comprising a system. In such cases, a developer has to first locate the implementation of

Aspect mining identifies cross-cutting concerns in a program to help migrating it to an aspect-oriented design. Such concerns may not exist from the beginning, but emerge over time. By analysing where developers add code to a program, our history-based aspect mining (HAM) identifies and ranks cross-cutting concerns. We evaluated the effectiveness of our approach with the history of three open-source projects. HAM scales up to industrial-sized projects: for example, we were able to identify a locking concern that cross-cuts 1 284 methods in Eclipse. Additionally, the precision of HAM increases with project size and history; for Eclipse, it reaches 90 % for the top-10 candidates. 1.

In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Computer Science