Correlated quality metrics extracted from a source code repository can be utilized to design a model to automatically predict defects in a software system. It is obvious that the extracted metrics will result in a highly unbalanced data, since the number of defects in a good quality software system should be far less than the number of normal instances. It is also a fact that the selection of the best discriminating features significantly improves the robustness and accuracy of a prediction model. Therefore, the contribution of this paper is twofold, first it selects the best discriminating features that help in accurately predicting a defect in a software component. Secondly, a cost-sensitive logistic regression and decision tree ensemble-based prediction models are applied to the best discriminating features for precisely predicting a defect in a software component. The proposed models are compared with the most recent schemes in the literature in terms of accuracy, area under the curve, and recall. The models are evaluated using 11 datasets and it is evident from the results and analysis that the performance of the proposed prediction models outperforms the schemes in the literature.

This paper investigates the impact of dyslexia on the reading and comprehension of computer program code. Drawing upon work from the fields of program comprehension, eye tracking, dyslexia, models of reading and dyslexia gaze behaviour, a set of hypotheses is developed with which to investigate potential differences in the gaze behaviour of programmers with dyslexia compared to typical programmers. The hypotheses posit that, in general terms, programmers with dyslexia will show gaze behaviour of longer duration and a greater number of fixations on program features than typical programmers. An experiment is described in which 28 programmers (14 with dyslexia, 14 without dyslexia) were asked to read and explain three simple computer programs. Eye tracking technology is used to capture the gaze behaviour of the programmers. Data analysis suggests that the code reading behaviour of programmers with dyslexia is not what would be expected based on the dyslexia literature relating to natural text. In conjunction with further exploratory analysis, observations are made in relation to spatial differences in how programmers with dyslexia read and scan code. The results show that the gaze behaviour of programmers with dyslexia requires further study to understand effects such as code layout, identifier naming and line length. A possible impact on dyslexia gaze behaviour is from the visual crowding of features in program code which might cause certain program features to receive less attention during a program comprehension task.

Information system development methodologies and supporting CASE technology (collectively referred to as CASE methodology) are being widely used in organizations to improve the productivity and quality of the software development process. This paper reviews the implementation lifecycle of CASE methodology and suggests that the post-implementation stage is the most critical for ensuring its successful use and incorporation within an organization. Effective management of the post-implementation stage requires CASE methodology to be considered as a socio-technical innovation. The paper describes the key post-implementation processes and mechanisms for the monitoring of these processes. Finally, a field study is outlined to illustrate how these concepts have been applied to the management of a CASE methodology in a software development organization. The focus of our discussion is the framework for post-implementation management – the specific implementation guidelines for CASE methodology resulting from any such exercise are beyond the scope of this paper.

A significant challenge for organisations is the timely identification of the abnormalities or deviations in their process executions. Abnormalities are generally due to missing vital aspects of a process or possession of unwanted behaviour in the process execution. Conformance analysis techniques examine the synchronisation between the recorded logs and the learned process models, but the exploitation of event logs for abnormality detection is a relatively under-explored area in process mining. In this paper, we proposed a novel technique for the identification of abnormalities in business process execution through the extension of available conformance analysis techniques. Non-traditional conformance analysis techniques are used to find correlations and discrepancies between simulated and observed behaviour in process logs. Initially, the raw event log is filtered into two variants, successful and failed, based upon the outcome of the instances. Successfully executed instances refer to an ideal conduct of process and are utilised to discover an optimal process model. Later, the process model is used as a behavioural benchmark to classify the abnormality in the failed instances. Abnormal behaviour is compiled grounded on three dimensions of conformance, control flow-based alignment, trace-level alignment and event-level alignment. For early predictions, we introduced the notion of conformance lifeline presenting the impact of varying fitness scores during process execution. We applied the proposed methodology to a real-world event log and presented several process-specific improvement measures in the discussion section.