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User-generated mobile application reviews have become a gold mine for timely identifying functional defects in this type of software artifacts. In this work, we develop a hidden structural SVM model for extracting detailed defect descriptions from user reviews at the sentence level. Structured features and constraints are introduced to reduce the demand of exhaustive manual annotation at the sentence...
Context: Software Bug Severity Classification can help to improve the software bug triaging process. However, severity levels present a high-level of data imbalance that needs to be taken into account. Aim: We investigate cost-sensitive strategies in multi-class bug severity classification to counteract data imbalance. Method: We transform datasets from three severity classification papers to a common...
In crowdsourced testing, it is beneficial to automatically classify the test reports that actually reveal a fault – a true fault, from the large number of test reports submitted by crowd workers. Most of the existing approaches toward this task simply leverage historical data to train a machine learning classifier and classify the new incoming reports. However, our observation on real industrial data...
Traditional software engineering programming paradigms are mostly object or procedure oriented, driven by deterministic algorithms. With the advent of deep learning and cognitive sciences there is an emerging trend for data-driven programming, creating a shift in the programming paradigm among the software engineering communities. Visualizing and interpreting the execution of a current large scale...
When there exists not enough historical defect data for building accurate prediction model, semi-supervised defect prediction (SSDP) and cross-project defect prediction (CPDP) are two feasible solutions. Existing CPDP methods assume that the available source data is well labeled. However, due to expensive human efforts for labeling a large amount of defect data, usually, we can only make use of the...
A key component of software testing is deciding whether a test case has passed or failed: an expensive and error-prone manual activity. We present an approach to automatically classify passing and failing executions using semi-supervised learning on dynamic execution data (test inputs/outputs and execution traces). A small proportion of the test data is labelled as passing or failing and used in conjunction...
The traditional duplicate bug reports detection approaches are usually based on vector space model. However, the experimental result is rarely satisfying since this method cannot distinguish semantic correlation among bug reports which written by natural languages. Topic model, as a method to model underlying topics of texts, can solve the problem of document similarity calculation methods used in...
Network defenders are locked in a constant race with attackers as they try to defend their networks. The defenders suffer from a huge disadvantage: they lack knowledge of the existence of zero-day vulnerabilities that have not been yet been discovered or publically disclosed, but that are still weakening the security of their networks. It would be a huge advantage to these defenders if they had some...
In these days, Employee turnover has become a major challenge in many software industries. Most often, people suffer from stress due to heavy work pressures imposed on them and competitive spirit of the work completed in their day to day lives. A survey was conducted by software professionals who work for various companies and stress on them was investigated. For this study, the PEGASOS optimization...
Software fault prediction (SFP) is useful for helping the software engineer to locate potential faulty modules in software testing more easily, so that it can save a lot of time and budgets to improve the software quality. In this paper, aiming at solving the problem that the faulty samples are too rare to train a classifier, an one-class SFP model is proposed by using only non-faulty samples based...
CONTEXT: Recent studies have shown that estimation accuracy can be affected by only using a window of recent projects (instead of all past projects) as training data for building an effort estimation model. The effect and its extent can be affected by the effort estimation methods used, and the windowing policy used (fixed size or fixed duration). The generality of the windowing approach remains uncertain,...
It is widely recognized that software process improvement (SPI) engineers need to be trained by software engineering groups to perform quality SPI activities. However, such engineers are required to have a wide range of skills, and therefore it is difficult to properly determine the scope and goal of training courses. To solve such problems, the group companies of Mitsubishi Electric Corporation have...
Software effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. Several estimation techniques have been developed in this regard and it is difficult to determine which model gives more accurate estimation on which dataset. Among all proposed methods, the Radial Basis Function Neural (RBFN) networks models have presented promising results in software...
Software effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. Several estimation techniques have been developed in this regard and it is difficult to determine which model gives more accurate estimation on which dataset. Among all proposed methods, the Radial Basis Function Neural (RBFN) networks models have presented promising results in software...
Defect prediction on new projects or projects with limited historical data is an interesting problem in software engineering. This is largely because it is difficult to collect defect information to label a dataset for training a prediction model. Cross-project defect prediction (CPDP) has tried to address this problem by reusing prediction models built by other projects that have enough historical...
To contribute software testing, and save testing costs, a wide range of machine learning approachs have been studied to predict defects in software modules. Unfortunately, the imbalanced nature of this type of data increases the learning difficulty of such a task. In this paper, we present UCRF, a method based on undersampling technique and conditional random field (CRF) for software defect prediction...
The emerging new data types bring tremendous challenges to data mining. There is an enormous amount of high-dimensional class-imbalanced data in different fields. In this case, traditional classification methods are not appropriate because they are prone to ensure the accuracy of the majority class. Meanwhile, the curse of dimensionality makes situations more complicated. Finding a complicated classifier...
Defect prediction techniques allow spotting modules (or commits) likely to contain (introduce) a defect by training models with product or process metrics -- thus supporting testing, code integration, and release decisions. When applied to processes where software changes rapidly, conventional techniques might fail, as trained models are not thought to evolve along with the software. In this study,...
The design of an operator-training computer simulator for chemical processes is very important in the process control and simulation area. In this paper, an operator-training simulator is developed for a small-scale fine chemical production process using an IPC (industrial personal computer and intelligent process controller) simulation mode and a typical SCADA (supervisory control and data acquisition)...
Software project managers need information such as cumulative number of failures present in a software after testing a certain period of time to determine release time of software. In this paper, an artificial neural network (ANN) based model which uses a new network architecture is proposed to predict cumulative number of failures in software. An extra layer is added between input layer and hidden...
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