How to achieve academic career success has been a long-standing research question in social science research. With the growing availability of large-scale well-documented academic profiles and career trajectories, scholarly interest in career success has been reinvigorated, which has emerged to be an active research domain called the Science of Science (i.e., SciSci). In this study, we adopt an innovative dynamic perspective to examine how individual and social factors will influence career success over time. We propose ACSeeker, an interactive visual analytics approach to explore the potential factors of success and how the influence of multiple factors changes at different stages of academic careers. We first applied a Multi-factor Impact Analysis framework to estimate the effect of different factors on academic career success over time. We then developed a visual analytics system to understand the dynamic effects interactively. A novel timeline is designed to reveal and compare the factor impacts based on the whole population. A customized career line showing the individual career development is provided to allow a detailed inspection. To validate the effectiveness and usability of ACSeeker, we report two case studies and interviews with a social scientist and general researchers.

How could soccer player migration impact national team performance, or vice versa? The answer to this question could play an essential role in making appropriate decisions and policies regarding the international mobility of soccer players. However, answering such a question faces two main challenges, including the complex relationship between variables in multi-attribute temporal data describing migrated players and national team performance, and the interpretation of analysis results in policymaking scenarios. In this work, we have closely collaborated with domain experts and characterized the problems of soccer player migration analysis. To address the first challenge, we adapt a cross-lagged panel analysis model into the player migration analysis problem. This cross-lagged panel analysis model is effective to evaluate the impact strength between player migration and national team performance, and straightforward to reveal the causal relationship. To address the second challenge, we design and develop a visual analytics system, MIG-Viewer, to help the experts to interpret the results of the proposed model efficiently. With MIG-Viewer, the experts can navigate the countries of interest in accordance with migration strategy, conduct comprehensive analysis with the comparison of impact strength, and adjust player migration and inspect further details of a specific country. We present two case studies using global player migration data since 1992 with three soccer analysis experts to demonstrate the effectiveness and usefulness of the system.

Accurate detection of software components that need to be exposed to additional verification and validation offers the path to high quality products while minimizing non essential software assurance expenditures. In this type of quality modeling we assume that software modules with known fault content developed in similar environment are available. Supervised learning algorithms are the traditional methods of choice for training on existing modules. The models are then used to predict fault content for newly developed software components prior to product release. However, one needs to realize that establishing whether a module contains a fault or not, only to be used for model training, can be expensive. The basic idea behind semi-supervised learning is to learn from a small number of software modules with known fault content and supplement model training with modules for which the fault information is not available, thus reducing the overall cost of quality assurance. In this study, we investigate the performance of semi-supervised learning for software fault prediction. A preprocessing strategy, multidimensional scaling, is embedded in the approach to reduce the dimensional complexity of software metrics used for prediction. Our results show that the dimension-reduction with semi-supervised learning algorithm preforms significantly better than one of the best performing supervised learning algorithm - random forest - in situations when few modules with known fault content are available. We compare our results with the published benchmarks and clearly demonstrate performance benefits.

Accurate detection of defects prior to product release helps software engineers focus verification activities on defect prone modules, thus improving the effectiveness of software development. A common scenario is to use the defects from prior releases to build the prediction model for the upcoming release, typically through a supervised learning method. As software development is a dynamic process, fault characteristics in subsequent releases may vary. Therefore, supplementing the defect information from prior releases with limited information about the defects from the current release detected early seems to offer intuitive and practical benefits. We propose active learning as a way to automate the development of models which improve the performance of defect prediction between successive releases. Our results show that the integration of active learning with uncertainty sampling consistently outperforms the corresponding supervised learning approach. We further improve the prediction performance with feature compression techniques, where feature selection or dimensionality reduction is applied to defect data prior to active learning. We observe that dimensionality reduction techniques, particularly multidimensional scaling with random forest similarity, work better than feature selection due to their ability to identify and combine essential information in data set features. We present the improvements offered by this methodology through the prediction of defective modules in the three successive versions of Eclipse.

Accurate detection of fault prone modules offers the path to high quality software products while minimizing non essential assurance expenditures. This type of quality modeling requires the availability of software modules with known fault content developed in similar environment. Establishing whether a module contains a fault or not can be expensive. The basic idea behind semi-supervised learning is to learn from a small number of software modules with known fault content and supplement model training with modules for which the fault information is not available. In this study, we investigate the performance of semi-supervised learning for software fault prediction. A preprocessing strategy, multidimensional scaling, is embedded in the approach to reduce the dimensional complexity of software metrics. Our results show that the semi-supervised learning algorithm with dimension-reduction preforms significantly better than one of the best performing supervised learning algorithms, random forest, in situations when few modules with known fault content are available for training.

This paper augments Boehm-Turner's model of agile and plan-based software development augmented with an AI search algorithm. The AI search finds the key factors that predict for the success of agile or traditional plan-based software developments. According to our simulations and AI search algorithm: (1) in no case did agile methods perform worse than plan-based approaches; (2) in some cases, agile performed best. Hence, we recommend that the default development practice for organizations be an agile method. The simplicity of this style of analysis begs the question: why is so much time wasted on evidence-less debates on software process when a simple combination of simulation plus automatic search can mature the dialog much faster?