Built to Last or Built Too Fast? Evaluating Prediction Models for Build Times

Automated builds are integral to the Continuous Integration (CI) software development practice. In CI, developers are encouraged to integrate early and often. However, long build times can be an issue when integrations are frequent. This research focuses on finding a balance between integrating often and keeping developers productive. We propose and analyze models that can predict the build time of a job. Such models can help developers to better manage their time and tasks. Also, project managers can explore different factors to determine the best setup for a build job that will keep the build wait time to an acceptable level. Software organizations transitioning to CI practices can use the predictive models to anticipate build times before CI is implemented. The research community can modify our predictive models to further understand the factors and relationships affecting build times.Comment: 4 paged version published in the Proceedings of the IEEE/ACM 14th International Conference on Mining Software Repositories (MSR) Pages 487-490. MSR 201

Implementing evaluation of the measurement process in an automotive manufacturer: a case study

Reducing process variability is presently an area of much interest in manufacturing organizations. Programmes such as Six Sigma robustly link the financial performance of the organization to the degree of variability present in the processes and products of the organization. Data, and hence measurement processes, play an important part in driving such programmes and in making key manufacturing decisions. In many organizations, however, little thought is given to the quality of the data generated by such measurement processes. By using potentially flawed data in making fundamental manufacturing decisions, the quality of the decision-making process is undermined and, potentially, significant costs are incurred. Research in this area is sparse and has concentrated on the technicalities of the methodologies available to assess measurement process capability. Little work has been done on how to operationalize such activities to give maximum benefit. From the perspective of one automotive company, this paper briefly reviews the approaches presently available to assess the quality of data and develops a practical approach, which is based on an existing technical methodology and incorporates simple continuous improvement tools within a framework which facilitates appropriate improvement actions for each process assessed. A case study demonstrates the framework and shows it to be sound, generalizable and highly supportive of continuous improvement goals

Investigating the impact of combining handwritten signature and keyboard keystroke dynamics for gender prediction

© 2019 IEEE. The use of soft-biometric data as an auxiliary tool on user identification is already well known. Gender, handorientation and emotional state are some examples which can be called soft-biometrics. These soft-biometric data can be predicted directly from the biometric templates. It is very common to find researches using physiological modalities for soft-biometric prediction, but behavioural biometric is often not well explored for this context. Among the behavioural biometric modalities, keystroke dynamics and handwriting signature have been widely explored for user identification, including some soft-biometric predictions. However, in these modalities, the soft-biometric prediction is usually done in an individual way. In order to fill this space, this study aims to investigate whether the combination of those two biometric modalities can impact the performance of a soft-biometric data, gender prediction. The main aim is to assess the impact of combining data from two different biometric sources in gender prediction. Our findings indicated gains in terms of performance for gender prediction when combining these two biometric modalities, when compared to the individual ones