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.

Software fault localization, which is an important software quality assurance technology, provides the location of the faults in software to improve the efficiency of debugging and repairing. In previous research, software fault localization techniques, such as spectrum-based, mutation-based, and program slicing, have been widely used and achieved good results. However, many statements could have same suspicious values by using these techniques, which will consume large amount of manual effort to confirm and affect the accuracy of fault localization. For example, using Ochiai or DStar to locate 395 faulty versions of 6 projects in Defects4J, nearly 70 % of the faulty versions have more than one suspicious statement are ranked as top tied 1. To address the above problem, this paper proposes a complexity-based fault localization (CBFL) technique to further improve the accuracy of fault localization. Firstly, a set of metrics for measuring the complexity of statements is proposed, and the metrics of each statement in projects are extracted to construct a classification model. Then, the classification model is used to predict the faulty probability of the statements which are ranked as top tied 1 by SBFL, MBFL or other techniques, and these statements are reranked according to the estimated faulty probability to improve the accuracy of fault localization. This paper implements a fault localization tool CDStar based on the CBFL, and conducts experiments on the Defects4J dataset. Comparing with the DStar, the results show that CBFL outperforms DStar in terms of Einspect @1 and EXAM.

Bugs dominate the corrective maintenance and evolutionary changes in large-scale software systems. The topic of bugs has been extensively investigated and reported in the literature. Unfortunately, the existential question of all "whether a reported bug will be fixed or not" has not received much attention. The paper presents an empirical study on four open source projects to examine the factors that influence the likelihood of a bug getting fixed or not. Overall, our study can be contextualized as a conceptual replication of a previous study on Microsoft systems from a commercial domain. The similarities and differences in terms of the design, execution, and results between the two studies are discussed. It was observed from these systems that the reputations of the reporter and assigned developer to fix it, and the number of comments on a bug have the most substantial impact on its probability to get fixed. Moreover, we formulated a predictive model from features available as soon as a bug is reported to estimate whether it will be fixed or not. Intra and inter (cross) project validations were performed. Precision and Recall metrics were used to assess the predictive model. Their values were recorded in the 60% to 70% range.

Change of Lake Area and water level reflects the space-time change of water resources objectively. Using LEGOS HYDROWEB lakes information data to extract water area and water level was not widely used. This paper use LEGOS HYDROWEB data of Qinghai-Tibet Plateau in the period of 1995-2014, 62 typical lakes water level change trend and 58 lakes area change tendency were analyzed, and according to its change trend these lakes can be divided into three types: growth, stable and recession, selected two typical lakes of each types for specific analysis. The results showed that the number of water level in growth type was 50, stable type has 9 lakes, and recession type has 3 lakes. The number of Area of growth type was 47, stable type has 9 lakes and recession type has 2 lakes.