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Performance regressions, such as a higher CPU utilization than in the previous version of an application, are caused by software application updates that negatively affect the performance of an application.Although a plethora of mining software repository research has been done to detect such regressions, research tools are generally not readily available to practitioners. Application Performance...
Metamorphic Testing (MT) has been demonstrated to successfully alleviate oracle problems in many areas, including machine learning, compilers, bioinformatics, etc. However, given a new MT task, it is still very challenging to identify enough Metamorphic Relations (MRs) which are key components of MT. Aiming at this problem, we revisited previous MT applications and realized that they could form a...
Software regression testing verifies previous features on a software product when it is modified or new features are added to it. Because of the nature of regression testing it is a costly process. Different approaches have been proposed to reduce the costs of this activity, among which are: minimization, prioritization, and selection of test cases. Recently, soft computing techniques, such as data...
Nowadays, APT attacks bring extreme threat and challenge to the network information security. Based on analysis of big data technique, the paper presents an APT security protective framework, which integrates deep and three-dimensional defense strategies, besides, the big data are used to explore and analyze possible APT attacks as well as threat positioning and tracks.
A product inspection management system has been developed to meet information management requirements of the inspection process and test data results. An algorithm for extracting empirical report data is proposed, combining the Word generation technology with multi-template application to speed up report editing. In depth study of storage data, queries and statistics functions are realized. System...
Software tend to evolve over time and so does the test-suite. Regression testing is aimed at assessing that the software evolution did not compromise the working of the existing software components. However, as the software and consequently the test-suite grow in size, the execution of the entire test-suite for each new build becomes infeasible. Techniques like test-suite selection, test-suite minimisation...
This research is motivated through the demand to create routing in indoor environment based on activity recognition approach. A model to discriminate between walking, climbing up stair, and climbing down stair is introduced. Data was collected from a group of participants performing walking up stairs, walking down stairs, and walking on normal path inside the building. 35 features are considered in...
In this paper, we motivate the utility of framing very common data analysis and business intelligence problems as a problem in understanding the differences between two datasets. We call this framework the Difference-of-Datasets (DoD) framework. We propose a simple and effective method to help find the root causes of changes, i.e. “Why did the observed change happen?” or “What drove the observed change?”...
In this paper, we present a new data mining framework for discovering sequence effects. In particular, we focus on the sequences consisting of actions that are taken in chronological order, like sequences of clinical procedures or marketing actions. Each sequence is associated with a binary outcome, a success or a failure. We investigate the hypothesis that certain subsequences of actions contribute...
In the past twenty years, progress in intrusion detection has been steady but slow. The biggest challenge is to detect new attacks in real time. In this work, a deep learning approach for anomaly detection using a Restricted Boltzmann Machine (RBM) and a deep belief network are implemented. Our method uses a one-hidden layer RBM to perform unsupervised feature reduction. The resultant weights from...
Crowdsourcing services make it possible to collect huge amount of annotations from less trained crowd workers in an inexpensive and efficient manner. However, unlike making binary or pairwise judgements, labeling complex structures such as ranked lists by crowd workers is subject to large variance and low efficiency, mainly due to the huge labeling space and the annotators' non-expert nature. Yet...
Outlier detection or anomaly detection is an important and challenging issue in data mining, even so in the domain of energy data mining where data are often collected in large amounts but with little labeled information. This paper presents a couple of online outlier detection algorithms based on principal component analysis. Novel algorithmic treatments are introduced to build incremental PCA and...
Latent Dirichlet allocation (LDA) and other modified topic models have become the prevalent tools for semantic analysis and text data mining. With the rapid development of the medical information, large amount of data has been accumulated in the form of text, while most of which recording in a confused structure. Based on LDA, this paper proposes a revised approach, which enlightens an idea of weighting...
Real handwriting authentication systems need a robust writer identification over a long time period. The paper analyzes signature sessions of the ATV-Signature Long Term Database (ATV-SLT DB). The database contains 6 sessions generated by 27 users over 15 month. The quality change of the verification results over a period of 15 month is examined. 64static and dynamic biometric features from the ATV-SLT...
Pivot methods have shown to be an effective solution to overcome the problem of unavailable large bilingual corpora in statistical machine translation. The representative approach of pivot methods is the phrase pivot translation which is based on common pivot phrases to produce connections between source-pivot and pivot-target phrase tables. Nevertheless, this approach produces insufficient connections...
This paper presents a data analytics approach for recovering test-pad information from images of printed circuit boards. The main aim is to obtain highly accurate information as input to a robotic flying probe tester. Such a tester is a mechatronic system that is able to perform a great variety of diagnostic testing on printed circuit boards without any additional circuit board documentation. In this...
Big data applications (e.g., Extract, Transform, and Load (ETL) applications) are designed to handle great volumes of data. However, processing such great volumes of data is time-consuming. There is a need to construct small yet effective test data sets during agile development of big data applications. In this paper, we apply a combinatorial test data generation approach to two real-world ETL applications...
Test smells have been defined as poorly designed tests and, as reported by recent empirical studies, their presence may negatively affect comprehension and maintenance of test suites. Despite this, there are no available automated tools to support identification and repair of test smells. In this paper, we firstly investigate developers' perception of test smells in a study with 19 participants. The...
“Transfer learning”: is the process of translating quality predictors learned in one data set to another. Transfer learning has been the subject of much recent research. In practice, that research means changing models all the time as transfer learners continually exchange new models to the current project. This paper offers a very simple “bellwether” transfer learner. Given N data sets, we find which...
App developers publish apps on different platforms, such as Google Play, App Store, and Windows Store, to maximize the user volumes and potential revenues. Due to the different characteristics of the platforms and the different user preference (e.g., Android is more customized than iOS), app testing cases on these three platforms should also be designed differently. Comprehensive app testing can be...
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