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arXiv 2018年

作者： Xue, Bai Fränzle, Martin Zhan, Naijun State Key Lab. of Computer Science Institute of Software CAS China University of Chinese Academy of Sciences Beijing China Carl von Ossietzky Universität Oldenburg Germany

In this paper we propose a convex programming based method to address a long-standing problem of inner-approximating backward reachable sets of state-constrained polynomial systems subject to time-varying uncertainties. The backward reachable set is a set of states, from which all trajectories starting will surely enter a target region at the end of a given time horizon without violating a set of state constraints in spite of the actions of uncertainties. It is equal to the zero sub-level set of the unique Lipschitz viscosity solution to a Hamilton-Jacobi partial differential equation (HJE). We show that inner-approximations of the backward reachable set can be formed by zero sub-level sets of its viscosity super-solutions. Consequently, we reduce the inner-approximation problem to a problem of synthesizing polynomial viscosity super-solutions to this HJE. Such a polynomial solution in our method is synthesized by solving a single semi-definite program. We also prove that polynomial solutions to the formulated semi-definite program exist and can produce a convergent sequence of inner-approximations to the interior of the backward reachable set in measure under appropriate assumptions. This is the main contribution of this work. Several illustrative examples demonstrate the merits of our approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Viscosity

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A reinforced hungarian algorithm for task allocation in global software development 29

A reinforced hungarian algorithm for task allocation in glob...

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29th International Conference on software Engineering and Knowledge Engineering, SEKE 2017

作者： Yu, Xiao Wu, Man Jia, Xiangyang Liu, Ye State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China School of Computer Science and Information Engineering HuBei University Wuhan China

ISBN: (纸本)1891706411

The allocation of software development tasks is a critical management activity in distributed development projects. One of the most important problem is to find the lowest-cost way to assign tasks in global software development, which can be solved by Hungarian algorithm. However, the original Hungarian algorithm only assume that a task can only be solved by one development site. The assumption is not agreed with the actual case where a software development task is usually be solved through a collab.ration among several sites. To address such an issue, this paper proposes a reinforced Hungarian algorithm (RHA) for task assignment in global software development. RHA consists of three major stages. First, RHA transforms a n×m cost matrix into two n×n cost matrix by adding (2n-m) virtual development sites. Second, RHA performs the original Hungarian algorithm on the two n×n cost matrix to get the optimal assignment results. Finally, RHA removes the (2n-m) virtual development sites and gets the final optimal assignment result for m tasks. Simulation results indicate that RHA is a viable approach for the task assignment problem in global software development.

关键词： software design

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A data filtering method based on agglomerative clustering 29

A data filtering method based on agglomerative clustering

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29th International Conference on software Engineering and Knowledge Engineering, SEKE 2017

作者： Yu, Xiao Zhang, Jiansheng Zhou, Peipei Liu, Jin State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China School of Computer Science and Information Engineering HuBei University Wuhan China

ISBN: (纸本)1891706411

Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, larger irrelevant crosscompany (CC) data usually makes it difficult to build a crosscompany defect prediction model with high performance. To address such issues, this paper proposes a data filtering method based on Agglomerative Clustering (DFAC) for cross-company defect prediction. First, DFAC combines within-company inst.nces and cross-company inst.nces and uses Agglomerative clustering algorithms to group these inst.nces. Second, DFAC selects sub-clusters which consist at least one WC inst.nce, and collects the CC inst.nces in the selected sub-clusters into a new CC data. Compared with existing data filter methods, the experimental results on 15 public PROMISE datasets show that DFAC increases PD value, reduces PF value and achieves higher G-measure and AUC values.

关键词： Forecasting

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Is the number of faults helpful for cross-company defect prediction? 29

Is the number of faults helpful for cross-company defect pre...

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29th International Conference on software Engineering and Knowledge Engineering, SEKE 2017

作者： Jing, Yiyang Zhang, Jiansheng Liu, Jin State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China School of Computer Science and Information Engineering HuBei University Wuhan China

ISBN: (纸本)1891706411

In the field of Cross-company Defect Prediction (CCDP), how to deal with the data to make it more accurately predict the cross-company software defects is the focus problem we need to consider. Now a mainst.eam idea is to determine the weight of the training data based on the similarity between the training data and the test set, and then build the model on the basis of these weighted data. However, sometimes, when we deal with some problems with imbalance class, directly using the weight calculated above may lead to errors. Because a large number of non-defective inst.nce's weight accumulation will lead to the defective inst.nce's weight has a little impact on the final result. This is why we need to consider the addition of the number of defects. Considering the number of defects will effectively eliminate the impact of a large number of non-defective inst.nce's weight accumulation. Therefore, we propose a Transfer-learning Naïve Bayes model considering the number of defective information(NTNB). The method consists of two major stages: Weight the data and build the prediction model. In the stage of weighting the data, we not only consider the similarity between the data but also consider the number of defects to get the final weights for data. And we conducted a set of comparative experiments on six open crosscompany datasets. The results show that considering the number of defects information can effectively avoid some defective inst.nce is misjudged as non-defective inst.nce, and improve the accuracy of prediction in some unbalanced problems.

关键词： Defects

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A comparative study of consistent snapshot algorithms for main-memory database systems

arXiv

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arXiv 2018年

作者： Li, Liang Wang, Guoren Wu, Gang Yuan, Ye Chen, Lei Lian, Xiang Department of Computer Science Northeastern University of China 100819 United States Department of Computer Science Beging Institute of Technology 100081 China Department of State Key Lab. for Novel Software Technology Nanjing University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Computer Science Kent State University United States

In-memory databases (IMDBs) are gaining increasing popularity in big data applications, where clients commit updates intensively. Specifically, it is necessary for IMDBs to have efficient snapshot performance to support certain special applications (e.g., consistent checkpoint, HTAP). Formally, the in-memory consistent snapshot problem refers to taking an in-memory consistent time-in-point snapshot with the constraints that 1) clients can read the latest data items and 2) any data item in the snapshot should not be overwritten. Various snapshot algorithms have been proposed in academia to trade off throughput and latency, but industrial IMDBs such as Redis adhere to the simple fork algorithm. To understand this phenomenon, we conduct comprehensive performance evaluations on mainst.eam snapshot algorithms. Surprisingly, we observe that the simple fork algorithm indeed outperforms the state-of-the-arts in update-intensive workload scenarios. On this basis, we identify the drawbacks of existing research and propose two lightweight improvements. Extensive evaluations on synthetic data and Redis show that our lightweight improvements yield better performance than fork, the current industrial standard, and the representative snapshot algorithms from academia. Finally, we have opensourced the implementation of all the above snapshot algorithms so that practitioners are able to benchmark the performance of each algorithm and select proper methods for different application scenarios. Copyright © 2018, The Authors. All rights reserved.

关键词： Database systems

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Optimal algorithm for profiling dynamic arrays with finite values

arXiv

引用

arXiv 2018年

作者： Yang, Dingcheng Yu, Wenjian Deng, Junhui Liu, Shenghua BNRist Dept. Computer Science & Tech. Tsinghua Univ. Beijing China CAS Key Lab. Network Data Science & Tech. Inst. Computing Technology Chinese Academy of Sciences Beijing China

How can one quickly answer the most and top popular objects at any time, given a large log stream in a system of billions of users? It is equivalent to find the mode and top-frequent elements in a dynamic array corresponding to the log stream. However, most existing work either restrain the dynamic array within a sliding window, or do not take advantages of only one element can be added or removed in a log stream. Therefore, we propose a profiling algorithm, named S-Profile, which is of O(1) time complexity for every updating of the dynamic array, and optimal in terms of computational complexity. With the profiling results, answering the queries on the statistics of dynamic array becomes trivial and fast. With the experiments of various settings of dynamic arrays, our accurate S-Profile algorithm outperforms the well-known methods, showing at least 2X speedup to the heap based approach and 13X or larger speedup to the balanced tree based approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Computational complexity

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Scalab.e tag recommendation for software information sites 24

Scalable tag recommendation for software information sites

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24th IEEE International Conference on software Analysis, Evolution, and Reengineering, SANER 2017

作者： Zhou, Pingyi Liu, Jin Yang, Zijiang Zhou, Guangyou State Key Lab. of Software Engineering Computer School Wuhan University China Department of Computer Science Western Michigan University Kalamazoo United States School of Computer Central China Normal University Wuhan China

ISBN: (纸本)9781509055012

software developers can search, share and learn development experience, solutions, bug fixes and open source projects in software information sites such as StackOverflow and Freecode. Many software information sites rely on tags to classify their contents, i.e. software objects, in order to improve the performance and accuracy of various operations on the sites. The quality of tags thus has a significant impact on the usefulness of these sites. High quality tags are expected to be concise and can describe the most important features of the software objects. © 2017 IEEE.

关键词： Open source software

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Sliding-window based Propagation-aware Temporal Verification for Monitoring Parallel Cloud Business Workflows

Sliding-window based Propagation-aware Temporal Verification...

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International Conference on computer Supported Cooperative Work in Design

作者： Yeguo Wang Rongbin Xu Futian Wang Haoyu Luo Menglong Wang Xiao Liu School of Computer Science and Technology Anhui University Hefei China Ministry of Education Anhui University Hefei China State Key Lab. of Software Engineering Wuhan University Wuhan China School of Info Technology Deakin University Melbourne australia

Dn-time completion is one of critical QoS (Quality of Service) measurements for massive time-constrained business processes executing in the cloud environment. However, temporal violations inevitably occur during the execution of business workflows due to the uncertainty and dynamic nature of cloud environment. To realize the goal of the target on-time completion rate, workflow temporal verification is employed for monitoring the execution time of workflow activities and handling time delays before the deadline. While current studies on business cloud workflows temporal verification mainly monitor the execution of workflows with fixed observation time intervals along the system timeline, which can result in huge monitoring cost. To reduce the monitoring cost, this paper presents a sliding-window based dynamic temporal checkpoint selection strategy using propagation-aware throughput temporal consistency model for parallel cloud business workflows. The strategy adjusts the next observation time interval according to the temporal verification result at the previous checkpoint. Experimental results show that our strategy can select fewer temporal checkpoints and violation handling points compared with conventional strategies under the same situations.

关键词： Conferences Collab.rative work

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FSCR:A Feature selection method for software defect prediction 29

FSCR:A Feature selection method for software defect predicti...

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29th International Conference on software Engineering and Knowledge Engineering, SEKE 2017

作者： Yu, Xiao Ma, Ziyi Ma, Chuanxiang Gu, Yi Liu, Ruiqi Zhang, Yan State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China School of Computer Science and Information Engineering HuBei University Wuhan China Educational Informationalization Engineering Research Center of HuBei Province Wuhan China International School of Software Wuhan University Wuhan China

ISBN: (纸本)1891706411

Prediction the number of faults in software modules can be more helpful inst.ad of predicting the modules being faulty or non-faulty. Some regression models have been used for predicting the number of faults. However, the software defect data may involve irrelevant and redundant module features, which will degrade the performance of these regression models. To address such issue, this paper proposes a feature selection method based on Feature Spectral Clustering and feature Ranking (FSCR) for the number of software faults prediction. First, FSCR groups the original features with spectral clustering according to the correlation between every two features. Second, FSCR employs ReliefF algorithm to compute the relevance between each feature with respect to the number of faults and selects top p most relevant features from each resulted cluster. We evaluate our proposed method on 6 widely-studied project datasets with four performance metrics. Comparison with five existing feature selection methods demonstrates that FSCR is effective in selecting features for the number of faults prediction.

关键词： Feature Selection

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How to reduce software development cost with personnel assignment optimization: Exemplary improvement on the Hungarian Algorithm 17

How to reduce software development cost with personnel assig...

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21st International Conference on Evaluation and Assessment in software Engineering, EASE 2017

作者： Wang, Chong Luo, Zhong Lin, Luxin Daneva, Maya State Key Lab. of Software Engineering Computer School Wuhan University Wuhan430072 China University of Twente Enschede7522HN Netherlands Science and Research Department Naval University of Engineering Wuhan430033 China Computer School Wuhan University Wuhan430072 China

ISBN: (纸本)9781450348041

Reasonable reduction and controlling of software cost is always a challenge for software companies. To estimate software development cost more precisely, current research effort is focused on improving the measurement of software size or complexity by combining or adjusting key cost drivers, such as function points and other observable project context factors. However, personnel factors are seldom investigated or treated in depth as a way to reduce the estimated software development cost. On the premise that a software project is decomposed in a number of tasks, and that predetermined developers are availab.e as resources for it, this paper intends to optimize the allocation of availab.e personnel for lower development cost. In this research, we consider the problem of allocating competent developers to suitable tasks as an unbalanced personnel assignment problem, and improve the traditional Hungarian Algorithm by applying three strategies to find optimal personnel allocation solutions for diverse requirements. Moreover, the performance of our improved algorithms is evaluated and compared through a series of experiments on simulation datasets to identify and validate the measurement indicators and influence factors of their performance. © 2017 ACM.

关键词： Cost estimating

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