Guangchun Luo

Transfer learning for cross-company software defect prediction

Context: Software defect prediction studies usually built models using within-company data, but very few focused on the prediction models trained with cross-company data. It is difficult to employ these models which are built on the within-company data in practice, because of the lack of these local data repositories. Recently, transfer learning has attracted more and more attention for building classifier in target domain using the data from related source domain. It is very useful in cases when distributions of training and test instances differ, but is it appropriate for cross-company software defect prediction? Objective: In this paper, we consider the cross-company defect prediction scenario where source and target data are drawn from different companies. In order to harness cross company data, we try to exploit the transfer learning method to build faster and highly effective prediction model. Method: Unlike the prior works selecting training data which are similar from the test data, we proposed a novel algorithm called Transfer Naive Bayes (TNB), by using the information of all the proper features in training data. Our solution estimates the distribution of the test data, and transfers cross-company data information into the weights of the training data. On these weighted data, the defect prediction model is built. Results: This article presents a theoretical analysis for the comparative methods, and shows the experiment results on the data sets from different organizations. It indicates that TNB is more accurate in terms of AUC (The area under the receiver operating characteristic curve), within less runtime than the state of the art methods. Conclusion: It is concluded that when there are too few local training data to train good classifiers, the useful knowledge from different-distribution training data on feature level may help. We are optimistic that our transfer learning method can guide optimal resource allocation strategies, which may reduce software testing cost and increase effectiveness of software testing process.

Adaptive Spraying for Routing in Delay Tolerant Networks

Message delivery in Delay Tolerant Networks (DTNs) is challenging due to the fact that the network is intermittently connected. Mobility can be exploited to improve DTN performance. In this paper, we propose a DTN routing scheme Adaptive Spraying. Adaptive Spraying exploits mobility pattern and encounter history to predict the number of nodes with no copy a node will encounter within the expected delay. The number of nodes encountered can be viewed as the number of copies disseminated. Each node with copies dynamically chooses the number of copies by itself, instead of a fixed number determined at the source node. We present an analysis of the scheme and validate the analytical results with simulations. Simulation results show that Adaptive Spraying performs well over a variety of environmental conditions such as transmission range and traffic load.

On Software Defect Prediction Using Machine Learning

This paper mainly deals with how kernel method can be used for software defect prediction, since the class imbalance can greatly reduce the performance of defect prediction. In this paper, two classifiers, namely, the asymmetric kernel partial least squares classifier (AKPLSC) and asymmetric kernel principal component analysis classifier (AKPCAC), are proposed for solving the class imbalance problem. This is achieved by applying kernel function to the asymmetric partial least squares classifier and asymmetric principal component analysis classifier, respectively. The kernel function used for the two classifiers is Gaussian function. Experiments conducted on NASA and SOFTLAB data sets using F-measure, Friedman’s test, and Tukey’s test confirm the validity of our methods.

Exploiting intercontact time for routing in delay tolerant networks

Because of the dynamic nature of delay tolerant networks (DTNs), many replication-based routing schemes were proposed to increase the probability of delivery by making multiple copies of each message. In such schemes, one concern is how many replicas of a message should be distributed in the network. In this paper, we propose a routing scheme for DTNs, called adaptive spraying based on the intercontact time (ASBIT). The scheme is based on the idea of that each node dynamically chooses the right number of message copies disseminated to respond to the current conditions of the network. When forwarding, ASBIT selects the node with a higher centrality as the next hop, and utilises the multi-attribute decision making theory for the division of the replication number between two nodes. Simulation results show that ASBIT achieves comparable delivery ratio and delivery delay while maintaining lower overhead compared with some well-known routing schemes in sparse scenarios. Copyright

Combining the requirement information for software defect estimation in design time

Abstract This paper analyzes the ability of requirement metrics for software defect prediction. Statistical significance tests are used to compare six machine learning algorithms on the requirement metrics, design metrics, and combination of both metrics in our analysis. The experimental results show the effectiveness of the predictor built on the combination of the requirement and design metrics in the early phase of the software development process.

Encounter-based routing in delay tolerant networks

Delay tolerant networks are intermittently connected. In this paper, we propose a DTN routing scheme EBR. The scheme is based on the idea of exploiting inter-contact time between mobile nodes to predict the number of nodes with no copy a node will encounter within the expected delay. In EBR, every node with message copies dynamically chooses the number of copies by itself. We evaluate the performance of EBR through various simulations. Simulation results show that EBR has good performance with low overhead in most scenarios.

A Layered Searchable Encryption Scheme with Functional Components Independent of Encryption Methods

Searchable encryption technique enables the users to securely store and search their documents over the remote semitrusted server, which is especially suitable for protecting sensitive data in the cloud. However, various settings (based on symmetric or asymmetric encryption) and functionalities (ranked keyword query, range query, phrase query, etc.) are often realized by different methods with different searchable structures that are generally not compatible with each other, which limits the scope of application and hinders the functional extensions. We prove that asymmetric searchable structure could be converted to symmetric structure, and functions could be modeled separately apart from the core searchable structure. Based on this observation, we propose a layered searchable encryption (LSE) scheme, which provides compatibility, flexibility, and security for various settings and functionalities. In this scheme, the outputs of the core searchable component based on either symmetric or asymmetric setting are converted to some uniform mappings, which are then transmitted to loosely coupled functional components to further filter the results. In such a way, all functional components could directly support both symmetric and asymmetric settings. Based on LSE, we propose two representative and novel constructions for ranked keyword query (previously only available in symmetric scheme) and range query (previously only available in asymmetric scheme).

Query-Biased Preview over Outsourced and Encrypted Data

For both convenience and security, more and more users encrypt their sensitive data before outsourcing it to a third party such as cloud storage service. However, searching for the desired documents becomes problematic since it is costly to download and decrypt each possibly needed document to check if it contains the desired content. An informative query-biased preview feature, as applied in modern search engine, could help the users to learn about the content without downloading the entire document. However, when the data are encrypted, securely extracting a keyword-in-context snippet from the data as a preview becomes a challenge. Based on private information retrieval protocol and the core concept of searchable encryption, we propose a single-server and two-round solution to securely obtain a query-biased snippet over the encrypted data from the server. We achieve this novel result by making a document (plaintext) previewable under any cryptosystem and constructing a secure index to support dynamic computation for a best matched snippet when queried by some keywords. For each document, the scheme has O(d) storage complexity and O(log(d/s) + s + d/s) communication complexity, where d is the document size and s is the snippet length.

Zone based Epidemic Routing in urban VANETs

Vehicular ad hoc networks (VANETs) are known for highly mobile and frequently disconnected characteristics. In this paper, we propose a new epidemic routing protocol in urban environment called Greedy Zone Epidemic Routing (GZER), in which neighbors of a vehicle are divided into different zones according to their physical locations. In each zone, the infection is decided by the zone summary vector. Simulation results show that GZER has compatible delivery ratio with the Epidemic Routing under low vehicle density or light packet traffic load settings, while it has a much better performance in all metrics under high vehicle density together with heavy packet traffic load settings.

Combating class imbalance problem in semi-supervised defect detection

Detection of defect-prone software modules is an important topic in software quality research, and widely studied under enough defect data circumstance. An improved semi-supervised learning approach for defect detection involving class imbalanced and limited labeled data problem has been proposed. This approach employs random under-sampling technique to resample the original training set and updating training set in each round for co-train style algorithm. In comparison with conventional machine learning approaches, our method has significant superior performance in the aspect of AUC (area under the receiver operating characteristic) metric. Experimental results also show that with the proposed learning approach, it is possible to design better method to tackle the class imbalanced problem in semi-supervised learning.