An Automatically Created Novel Bug Dataset and its Validation in Bug Prediction

Bugs are inescapable during software development due to frequent code changes, tight deadlines, etc.; therefore, it is important to have tools to find these errors. One way of performing bug identification is to analyze the characteristics of buggy source code elements from the past and predict the present ones based on the same characteristics, using e.g. machine learning models. To support model building tasks, code elements and their characteristics are collected in so-called bug datasets which serve as the input for learning. We present the \emph{BugHunter Dataset}: a novel kind of automatically constructed and freely available bug dataset containing code elements (files, classes, methods) with a wide set of code metrics and bug information. Other available bug datasets follow the traditional approach of gathering the characteristics of all source code elements (buggy and non-buggy) at only one or more pre-selected release versions of the code. Our approach, on the other hand, captures the buggy and the fixed states of the same source code elements from the narrowest timeframe we can identify for a bug's presence, regardless of release versions. To show the usefulness of the new dataset, we built and evaluated bug prediction models and achieved F-measure values over 0.74

Exploiting Abstract Syntax Trees to Locate Software Defects

Context. Software defect prediction aims to reduce the large costs involved with faults in a software system. A wide range of traditional software metrics have been evaluated as potential defect indicators. These traditional metrics are derived from the source code or from the software development process. Studies have shown that no metric clearly out performs another and identifying defect-prone code using traditional metrics has reached a performance ceiling. Less traditional metrics have been studied, with these metrics being derived from the natural language of the source code. These newer, less traditional and finer grained metrics have shown promise within defect prediction. Aims. The aim of this dissertation is to study the relationship between short Java constructs and the faultiness of source code. To study this relationship this dissertation introduces the concept of a Java sequence and Java code snippet. Sequences are created by using the Java abstract syntax tree. The ordering of the nodes within the abstract syntax tree creates the sequences, while small sub sequences of this sequence are the code snippets. The dissertation tries to find a relationship between the code snippets and faulty and non-faulty code. This dissertation also looks at the evolution of the code snippets as a system matures, to discover whether code snippets significantly associated with faulty code change over time. Methods. To achieve the aims of the dissertation, two main techniques have been developed; finding defective code and extracting Java sequences and code snippets. Finding defective code has been split into two areas - finding the defect fix and defect insertion points. To find the defect fix points an implementation of the bug-linking algorithm has been developed, called S + e . Two algorithms were developed to extract the sequences and the code snippets. The code snippets are analysed using the binomial test to find which ones are significantly associated with faulty and non-faulty code. These techniques have been performed on five different Java datasets; ArgoUML, AspectJ and three releases of Eclipse.JDT.core Results. There are significant associations between some code snippets and faulty code. Frequently occurring fault-prone code snippets include those associated with identifiers, method calls and variables. There are some code snippets significantly associated with faults that are always in faulty code. There are 201 code snippets that are snippets significantly associated with faults across all five of the systems. The technique is unable to find any significant associations between code snippets and non-faulty code. The relationship between code snippets and faults seems to change as the system evolves with more snippets becoming fault-prone as Eclipse.JDT.core evolved over the three releases analysed. Conclusions. This dissertation has introduced the concept of code snippets into software engineering and defect prediction. The use of code snippets offers a promising approach to identifying potentially defective code. Unlike previous approaches, code snippets are based on a comprehensive analysis of low level code features and potentially allow the full set of code defects to be identified. Initial research into the relationship between code snippets and faults has shown that some code constructs or features are significantly related to software faults. The significant associations between code snippets and faults has provided additional empirical evidence to some already researched bad constructs within defect prediction. The code snippets have shown that some constructs significantly associated with faults are located in all five systems, and although this set is small finding any defect indicators that transfer successfully from one system to another is rare

Animating the evolution of software

The use and development of open source software has increased significantly in the last decade. The high frequency of changes and releases across a distributed environment requires good project management tools in order to control the process adequately. However, even with these tools in place, the nature of the development and the fact that developers will often work on many other projects simultaneously, means that the developers are unlikely to have a clear picture of the current state of the project at any time. Furthermore, the poor documentation associated with many projects has a detrimental effect when encouraging new developers to contribute to the software. A typical version control repository contains a mine of information that is not always obvious and not easy to comprehend in its raw form. However, presenting this historical data in a suitable format by using software visualisation techniques allows the evolution of the software over a number of releases to be shown. This allows the changes that have been made to the software to be identified clearly, thus ensuring that the effect of those changes will also be emphasised. This then enables both managers and developers to gain a more detailed view of the current state of the project. The visualisation of evolving software introduces a number of new issues. This thesis investigates some of these issues in detail, and recommends a number of solutions in order to alleviate the problems that may otherwise arise. The solutions are then demonstrated in the definition of two new visualisations. These use historical data contained within version control repositories to show the evolution of the software at a number of levels of granularity. Additionally, animation is used as an integral part of both visualisations - not only to show the evolution by representing the progression of time, but also to highlight the changes that have occurred. Previously, the use of animation within software visualisation has been primarily restricted to small-scale, hand generated visualisations. However, this thesis shows the viability of using animation within software visualisation with automated visualisations on a large scale. In addition, evaluation of the visualisations has shown that they are suitable for showing the changes that have occurred in the software over a period of time, and subsequently how the software has evolved. These visualisations are therefore suitable for use by developers and managers involved with open source software. In addition, they also provide a basis for future research in evolutionary visualisations, software evolution and open source development