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| 1 | Source code fragment summarization with small-scale crowdsourcing based features显示文摘 | Najam NAZAR He JIANG Guojun GAO Tao ZHANG Xiaochen LI Zhilei REN | 2016 | Frontiers of Computer Science2016，10，3： | 5 |
| 2 | Extracting elite pairwise constraints for clustering 显示文摘 | Jiang He Ren Zhilei Xuan Jifeng | 2013 | Neurocomput- ing2013，99，1： | 1 |
| 3 | New Insights Into Diversification ofHyper-Heuristics 显示文摘 | Zhilei Ren He Jiang Jifeng Xuan etal | 2014 | IEEE Transactions on Cybernetics2014，44，10： | 1 |
| 4 | Mining authorship characteristics in bug repositories显示文摘Bug reports are widely employed to facilitate software tasks in software maintenance. Since bug reports are contributed by people, the authorship characteristics of contributors may heavily impact the performance of resolving software tasks. Poorly written bug reports may delay developers when fixing bugs. However,no in-depth investigation has been conducted over the authorship characteristics. In this study, we first leverage byte-level N-grams to model the authorship characteristics and employ Normalized Simplified Profile Intersection(NSPI) to identify the similarity of the authorship characteristics. Then, we investigate a series of properties related to contributors' authorship characteristics, including the evolvement over time and the variation among distinct products in open source projects. Moreover, we show how to leverage the authorship characteristics to facilitate a well-known task in software maintenance, namely Bug Report Summarization(BRS). Experiments on open source projects validate that incorporating the authorship characteristics can effectively improve a stateof-the-art method in BRS. Our findings suggest that contributors should retain stable authorship characteristics and the authorship characteristics can assist in resolving software tasks. | He JIANG Jingxuan ZHANG Hongjing MA Najam NAZAR Zhilei REN | 2017 | Science China(Information Sciences)2017，60，1： | 1 |
| 5 | Developer recommendation on bug commenting:a ranking approach for the developer crowd显示文摘A bug tracking system provides a collaborative platform for the developer crowd. After a bug report is submitted, developers can make comments to supplement the details of the bug report. Due to the large number of developers and bug reports, it is hard to determine which developer(also called commenter) is able to comment on a particular bug report. We refer to the problem of recommending developers for commenting on bug reports as commenter recommendation. In this paper, we perform an empirical analysis on commenter recommendation based on five-year bug reports of four open source projects. First, we preliminarily analyze bug comments and commenters in three categories, the relationship between commenters and fixers, the data scale of comments, and the collaboration on bug commenting. Second, we design a recommendation approach via ranking developers in the crowd to reduce the manual effort of identifying commenters. In this approach,we formulize the commenter recommendation problem as a multi-label recommendation task and leverage both developer collaboration and bug content to find out appropriate commenters. Experimental results show that our approach can effectively recommend commenters; 41% to 75% of the recall value is achieved for top-10 recommendation. Our empirical analysis on bug commenting can help developers understand and improve the process of fixing bugs. | Jifeng XUAN He JIANG Hongyu ZHANG Zhilei REN | 2017 | Science China(Information Sciences)2017，60，7： | 1 |
| 6 | Feature based problem hardness understanding for requirements engineering显示文摘Heuristics and metaheuristics have achieved great accomplishments in various fields, and the investigation of the relationship between these algorithms and the problem hardness has been a hot topic in the research field. Related research work has contributed much to the understanding of the underlying mechanisms of the algorithms for problem solving. However, most existing studies consider traditional combinatorial problems as their case studies. In this study, taking the Next Release Problem(NRP) from the requirements engineering as a case study, we investigate the relationship between software engineering problem instances and heuristics. We employ an evolutionary algorithm to evolve NRP instances, which are uniquely hard or easy for the target heuristic(Greedy Randomized Adaptive Search Procedure and Randomized Hill Climbing in this paper). Then, we use a feature-based method to estimate the hardness of the evolved instances, with respect to the target heuristic. Experimental results demonstrate that, evolutionary algorithm can be used to evolve NRP instances that are uniquely hard or easy to solve. Moreover, the features enable the estimation of the target heuristics' performance. | Zhilei REN He JIANG Jifeng XUAN Shuwei ZHANG Zhongxuan LUO | 2017 | Science China(Information Sciences)2017，60，3： | 0 |
| 7 | Compiler testing: a systematic literature analysis显示文摘Compilers are widely-used infrastructures in accelerating the software development,and expected to be trustworthy.In the literature,various testing technologies have been proposed to guarantee the quality of compilers.However,there remains an obstacle to comprehensively characterize and understand compiler testing.To overcome this obstacle,we propose a literature analysis framework to gain insights into the compiler testing area.First,we perform an extensive search to construct a dataset related to compiler testing papers.Then,we conduct a bibliometric analysis to analyze the productive authors,the influential papers,and the frequently tested compilers based on our dataset.Finally,we utilize association rules and collaboration networks to mine the authorships and the communities of interests among researchers and keywords.Some valuable results are reported.We find that the USA is the leading country that contains the most influential researchers and institutions.The most active keyword is“random testing”.We also find that most researchers have broad interests within small-scale collaborators in the compiler testing area. | Yixuan TANG Zhilei REN Weiqiang KONG He JIANG | 2020 | Frontiers of Computer Science2020，14，1： | 0 |
| 8 | Toward accurate detection on change barriers显示文摘In software development,it is easy to introduce code smells owing to the complexity of projects and the negligence of programmers.Code smells reduce code comprehensibility and maintainability,making programs error-prone.Hence,code smell detection is extremely important.Recently,machine learning-based technologies turn to be the mainstream detection approaches,which show promising performance.However,existing machine learning methods have two limitations:(1)most studies only focus on common smells,and(2)the proposed metrics are not effective when being used for uncommon code smell detection,e.g.,change barrier based code smells.To overcome these limitations,this paper investigates the detection of uncommon change barrier based code smells.We study three typical code smells,i.e.,Divergent Change,Shotgun Surgery,and Parallel Inheritance,which all belong to change barriers.We analyze the characteristics of change barriers and extract domain-specific metrics to train a Logistic Regression model for detection.The experimental results show that our method achieves 81.8%–100%precision and recall,outperforming existing algorithms by 10%–30%.In addition,we analyze the correlation and importance of the utilized metrics.We find our domain-specific metrics are important for the detection of change barriers.The results would help practitioners better design detection tools for such code smells. | Tingting LV Zhilei REN Xiaochen LI Guojun GAO He JIANG | 2021 | Science China(Information Sciences)2021，64，3： | 0 |
| 9 | Micro/nano structured oleophobic agent improving the wellbore stability of shale gas wells显示文摘Through embedding modified nano-silica particles on the surface of polystyrene using the method of Pickering emulsion polymerization,a kind of nano/micro oleophobic agent named OL-1 was developed.The effects of OL-1 on the rock surface properties and its performance in inhibiting the oil phase imbibition into the rock were explored.The performance and mechanisms of OL-1 in improving the wellbore stability of shale gas wells were evaluated and analyzed.OL-1 could absorb on the surface of the shale core to form a membrane with a micro-nano two-stage roughness,making the surface energy of the core decrease to 0.13 mN/m and the contact angle of the white oil on the core surface increase from 16.39°to 153.03°.Compared with the untreated capillary tube,when immersed into 3#white oil,the capillary tube treated by OL-1 had a reversal of capillary pressure from 273.76 Pa to-297.71 Pa,and the oil imbibition height inside the capillary tube decreased from 31 mm above the external liquid level to 33 mm below the external liquid level.The amount of oil invading into the rock core modified by OL-1 decreased by 64.29%compared with the untreated one.The shale core immersed into the oil-based drilling fluids with 1%OL-1 had a porosity reduction rate of only 4.5%.Compared with the core immersed in the drilling fluids without OL-1,the inherent force of the core treated by 1%OL-1 increased by 24.9%,demonstrating that OL-1 could effectively improve the rock mechanical stability by inhibiting oil phase imbibition. | GENG Yuan SUN Jinsheng CHENG Rongchao QU Yuanzhi ZHANG Zhilei WANG Jianhua WANG Ren YAN Zhiyuan REN Han WANG Jianlong | 2022 | Petroleum Exploration and Development2022，49，6： | 0 |
| 10 | Misleading classification显示文摘In this paper,we investigate a new problem–misleading classification in which each test instance is associated with an original class and a misleading class.Its goal for the data owner is to form the training set out of candidate instances such that the data miner will be misled to classify those test instances to their misleading classes rather than original classes.We discuss two cases of misleading classification.For the case where the classification algorithm is unknown to the data owner,a KNN based Ranking Algorithm(KRA)is proposed to rank all candidate instances based on the similarities between candidate instances and test instances.For the case where the classification algorithm is known,we propose a Greedy Ranking Algorithm(GRA)which evaluates each candidate instance by building up a classifier to predict the test set.In addition,we also show how to accelerate GRA in an incremental way when naive Bayes is employed as the classification algorithm.Experiments on 16 UCI data sets indicated that the ranked candidate instances by KRA can achieve promising leaking and misleading rates.When the classification algorithm is known,GRA can dramatically outperform KRA in terms of leaking and misleading rates though more running time is required. | JIANG He XUAN JiFeng REN ZhiLei WU YouXi WU XinDong | 2014 | Science China(Information Sciences)2014，57，5： | 0 |
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