A Framework for Exploiting Emergent Behaviour to capture 'Best Practice' within a Programming Domain

Inspection is a formalised process for reviewing an artefact in software engineering. It is proven to significantly reduce defects, to ensure that what is delivered is what is required, and that the finished product is effective and robust. Peer code review is a less formal inspection of code, normally classified as inadequate or substandard Inspection. Although it has an increased risk of not locating defects, it has been shown to improve the knowledge and programming skills of its participants. This thesis examines the process of peer code review, comparing it to Inspection, and attempts to describe how an informal code review can improve the knowledge and skills of its participants by deploying an agent oriented approach. During a review the participants discuss defects, recommendations and solutions, or more generally their own experience. It is this instant adaptability to new 11 information that gives the review process the ability to improve knowledge. This observed behaviour can be described as the emergent behaviour of the group of programmers during the review. The wider distribution of knowledge is currently only performed by programmers attending other reviews. To maximise the benefits of peer code review, a mechanism is needed by which the findings from one team can be captured and propagated to other reviews / teams throughout an establishment. A prototype multi-agent system is developed with the aim of capturing the emergent properties of a team of programmers. As the interactions between the team members is unstructured and the information traded is dynamic, a distributed adaptive system is required to provide communication channels for the team and to provide a foundation for the knowledge shared. Software agents are capable of adaptivity and learning. Multi-agent systems are particularly effective at being deployed within distributed architectures and are believed to be able to capture emergent behaviour. The prototype system illustrates that the learning mechanism within the software agents provides a solid foundation upon which the ability to detect defects can be learnt. It also demonstrates that the multi-agent approach is apposite to provide the free flow communication of ideas between programmers, not only to achieve the sharing of defects and solutions but also at a high enough level to capture social information. It is assumed that this social information is a measure of one element of the review process's emergent behaviour. The system is capable of monitoring the team-perceived abilities of programmers, those who are influential on the programming style of others, and the issues upon III which programmers agree or disagree. If the disagreements are classified as unimportant or stylistic issues, can it not therefore be assumed that all agreements are concepts of "Best Practice"? The conclusion is reached that code review is not a substandard Inspection but is in fact complementary to the Inspection model, as the latter improves the process of locating and identifying bugs while the former improves the knowledge and skill of the programmers, and therefore the chance of bugs not being encoded to start with. The prototype system demonstrates that it is possible to capture best practice from a review team and that agents are well suited to the task. The performance criteria of such a system have also been captured. The prototype system has also shown that a reliable level of learning can be attained for a real world task. The innovative way of concurrently deploying multiple agents which use different approaches to achieve the same goal shows remarkable robustness when learning from small example sets. The novel way in which autonomy is promoted within the agents' design but constrained within the agent community allows the system to provide a sufficiently flexible communications structure to capture emergent social behaviour, whilst ensuring that the agents remain committed to their own goals

AN INVESTIGATION OF COMMON CODING ERRORS IN OPEN SOURCE GRAPHICAL USER INTERFACE CODE

Introduction: This thesis investigates the occurrence of coding errors in the Open Source Software (OSS) Graphical User Interface (GUI) code. Characteristics of coding errors in the OSS GUI code are explored and analyzed so that guidelines are proposed to lower the influence of coding errors in OSS GUI software. Background: This thesis recognizes the increased prominence of, as well as the increased total volume of OSS GUI code within modern software applications. This thesis seeks to identify whether specific types of errors manifest themselves more frequently in GUI code. The rationale behind this investigation is that: if specific errors are known to occur in specific locations then they can be more easily identified; if specific errors are due to specific causes then they can be more easily recognized in earlier stages of software development. Methods: Common coding errors were selected and examined in example OSS code using an automatic code inspection. An analysis of results from this inspection identified the frequency and location (i.e. in GUI or non-GUI code) of the common coding error. An initial sample of OSS GUI projects was selected to be examined and a wider range of OSS GUI projects was randomly chosen to evaluate the results that were obtained from the initial sample. Results: It was found that there are some differences in the type of errors within differing portions of source code. Certain types of coding errors were more frequently identified in GUI code than non-GUI code and corresponding typical GUI coding error-prone behaviors were summarized. Discussion: Awareness of these differences helps predict errors during the earlier stages of software development. Comprehension of these GUI coding error-prone behaviours contributes to prevent typical GUI coding errors as much as possible during the whole lifecycle of OSS GUI projects

Meta-data to enhance case-based prediction.

The focus of this thesis is to measure the regularity of case bases used in Case-Based Prediction (CBP) systems and the reliability of their constituent cases prior to the system's deployment to influence user confidence on the delivered solutions. The reliability information, referred to as meta-data, is then used to enhance prediction accuracy. CBP is a strain of Case-Based Reasoning (CBR) that differs from the latter only in the solution feature which is a continuous value. Several factors make implementing such systems for prediction domains a challenge. Typically, the problem and solution spaces are unbounded in prediction problems that make it difficult to determine the portions of the domain represented by the case base. In addition, such problem domains often exhibit complex and poorly understood interactions between features and contain noise. As a result, the overall regularity in the case base is distorted which poses a hindrance to delivery of good quality solutions. Hence in this research, techniques have been presented that address the issue of irregularity in case bases with an objective to increase prediction accuracy of solutions. Although, several techniques have been proposed in the CBR literature to deal with irregular case bases, they are inapplicable to CBP problems. As an alternative, this research proposes the generation of relevant case-specific meta-data. The meta-data is made use of in Mantel's randomisation test to objectively measure regularity in the case base. Several novel visualisations using the meta-data have been presented to observe the degree of regularity and help identify suspect unreliable cases whose reuse may very likely yield poor solutions. Further, performances of individual cases are recorded to judge their reliability, which is reflected upon before selecting them for reuse along with their distance from the problem case. The intention is to overlook unreliable cases in favour of relatively distant yet more reliable ones for reuse to enhance prediction accuracy. The proposed techniques have been demonstrated on software engineering data sets where the aim is to predict the duration of a software project on the basis of past completed projects recorded in the case base. Software engineering is a human-centric, volatile and dynamic discipline where many unrecorded factors influence productivity. This degrades the regularity in case bases where cases are disproportionably spread out in the problem and solution spaces resulting in erratic prediction quality. Results from administering the proposed techniques were helpful to gain insight into the three software engineering data sets used in this analysis. The Mantel's test was very effective at measuring overall regularity within a case base, while the visualisations were learnt to be variably valuable depending upon the size of the data set. Most importantly, the proposed case discrimination system, that intended to reuse only reliable similar cases, was successful at increasing prediction accuracy for all three data sets. Thus, the contributions of this research are some novel approaches making use of meta-data to firstly provide the means to assess and visualise irregularities in case bases and cases from prediction domains and secondly, provide a method to identify unreliable cases to avoid their reuse in favour to more reliable cases to enhance overall prediction accuracy

Meta-data to enhance case-based prediction

The focus of this thesis is to measure the regularity of case bases used in Case-Based Prediction (CBP) systems and the reliability of their constituent cases prior to the system's deployment to influence user confidence on the delivered solutions. The reliability information, referred to as meta-data, is then used to enhance prediction accuracy. CBP is a strain of Case-Based Reasoning (CBR) that differs from the latter only in the solution feature which is a continuous value. Several factors make implementing such systems for prediction domains a challenge. Typically, the problem and solution spaces are unbounded in prediction problems that make it difficult to determine the portions of the domain represented by the case base. In addition, such problem domains often exhibit complex and poorly understood interactions between features and contain noise. As a result, the overall regularity in the case base is distorted which poses a hindrance to delivery of good quality solutions. Hence in this research, techniques have been presented that address the issue of irregularity in case bases with an objective to increase prediction accuracy of solutions. Although, several techniques have been proposed in the CBR literature to deal with irregular case bases, they are inapplicable to CBP problems. As an alternative, this research proposes the generation of relevant case-specific meta-data. The meta-data is made use of in Mantel's randomisation test to objectively measure regularity in the case base. Several novel visualisations using the meta-data have been presented to observe the degree of regularity and help identify suspect unreliable cases whose reuse may very likely yield poor solutions. Further, performances of individual cases are recorded to judge their reliability, which is reflected upon before selecting them for reuse along with their distance from the problem case. The intention is to overlook unreliable cases in favour of relatively distant yet more reliable ones for reuse to enhance prediction accuracy. The proposed techniques have been demonstrated on software engineering data sets where the aim is to predict the duration of a software project on the basis of past completed projects recorded in the case base. Software engineering is a human-centric, volatile and dynamic discipline where many unrecorded factors influence productivity. This degrades the regularity in case bases where cases are disproportionably spread out in the problem and solution spaces resulting in erratic prediction quality. Results from administering the proposed techniques were helpful to gain insight into the three software engineering data sets used in this analysis. The Mantel's test was very effective at measuring overall regularity within a case base, while the visualisations were learnt to be variably valuable depending upon the size of the data set. Most importantly, the proposed case discrimination system, that intended to reuse only reliable similar cases, was successful at increasing prediction accuracy for all three data sets. Thus, the contributions of this research are some novel approaches making use of meta-data to firstly provide the means to assess and visualise irregularities in case bases and cases from prediction domains and secondly, provide a method to identify unreliable cases to avoid their reuse in favour to more reliable cases to enhance overall prediction accuracy.EThOS - Electronic Theses Online ServiceGBUnited Kingdo