The correlation coefficient is commonly used in analyses of software project data sets for the purpose of quantifying the relationship between two variables. However, while there are various types of relationships between two variables, the correlation coefficient cannot distinguish between these types. This study proposes new metrics between two continuous variables that have the potential to characterize the relationship types.

Software quality assurance (SQA) is a series of activities within the software development lifecycle that repetitively verify or test the software deliverables to ensure their quality. In this paper, we propose a simulation model of SQA to quantitatively demonstrate the positive effect of adding quality assurance (QA) effort especially in early phases of software development. The proposed model can represent the relationship among the number of bugs in each phase, the amount of QA effort, the expected number of detectable bugs and the amount of bug fixing effort. The model can simulate the different QA strategies in a given software development context; thus, it is useful to identify the best or better strategies to improve software quality with smaller QA and bug fixing effort.

E-learning offers many advantages like being economical, flexible and customizable, but also has challenging aspects such as lack of – social-interaction, which results in contemplation and sense of remoteness. To overcome these and sustain learners’ motivation, various stimuli can be incorporated. Nevertheless, such adjustments initially require an assessment of engagement level. In this respect, we propose estimating engagement level from facial landmarks exploiting the facts that (i) perceptual decoupling is promoted by blinking during mentally demanding tasks; (ii) eye strain increases blinking rate, which also scales with task disengagement; (iii) eye aspect ratio is in close connection with attentional state and (iv) users’ head position is correlated with their level of involvement. Building empirical models of these actions, we devise a probabilistic estimation framework. Our results indicate that high and low levels of engagement are identified with considerable accuracy, whereas medium levels are inherently more challenging, which is also confirmed by inter-rater agreement of expert coders.

This study explores estimation of grasping region of objects from gaze data. Our study distinguishes from previous works by accounting for "grasping uniformity" of the objects. In particular, we consider three types of graspable objects: (i) with a well-defined graspable part (e.g. handle), (ii) without a grip but with an intuitive grasping region, (iii) without any grip or intuitive grasping region. We assume that these types define how "uniform" grasping region is across different graspers. In experiments, we use "Learning to grasp" data set and apply the method of [Pramot et al. 2018] for estimating grasping region from gaze data. We compute similarity of estimations and ground truth annotations for the three types of objects regarding subjects (a) who perform free viewing and (b) who view the images with the intention of grasping. In line with many previous studies, similarity is found to be higher for non-graspers. An interesting finding is that the difference in similarity (between free viewing and motivated to grasp) is higher for type-iii objects; and comparable for type-i and ii objects. Based on this, we believe that estimation of grasping region from gaze data offers a larger potential to "learn" particularly grasping of type-iii objects.

The goal of this paper is to improve the estimation performance of software development effort by mitigating the problem caused by outliers in a historical software project data set, which is used to construct an effort estimation model. To date, outlier removal methods have been proposed to solve this problem; however, they are not always effective because removing outliers reduces the number of data points (= software projects in our case) in a data set, and a model built from a small data set often suffers from lack of generality. In such a case, estimation performance can become even worse. In this paper we propose a method called data smoothing to mitigate the problem of outliers without reducing the number of data points. We consider that data points are outliers if they do not meet the assumption of Analogy-Based Estimation (ABE) such that “projects with similar features require similar development efforts.” The proposed method changes the effort values (person-months or person-hours) in a data set so as to satisfy this assumption; and by this way, all outliers become non-outliers without decreasing the data points. As a result of experimental evaluation using 8 software development data sets, we found that the proposed data smoothing showed the same or higher effort estimation accuracy than the non-smoothing case, while conventional outlier removal method showed worse accuracy in some data set.

While various software obfuscation techniques have been proposed to protect software, new types of threats keep emerging such as the symbolic execution attacks. Such attacks automatically analyze programs and are not accounted for by many of the existing obfuscation methods. Nevertheless, several methods against symbolic execution attacks exist such as linear obfuscation methods relying on Collatz conjuncture or obfuscation methods based on one-way hash functions. However, these methods bear several issues. Namely, linear obfuscation is weak against manual analysis due to its deterministic output. On the other hand, SHA-1 requires significant computational cost; and thus, it can be applied to only a limited number of targets. Therefore, in this research, we propose to employ a combination of several computationally cheap (arithmetic) obfuscating operations for preventing symbolic execution attacks. Through an experiment using angr and KLEE as symbolic execution tools, we demonstrate that obfuscation operation using array reference, bit rotation and XOR effectively prevents symbolic execution attacks at a low computational cost.

The effectiveness of defect prediction depends on modeling techniques as well as their parameter optimization, data preprocessing and ensemble development. This paper focuses on auto-sklearn, which is a recently-developed software library for automated machine learning, that can automatically select appropriate prediction models, hyperparameters and data preprocessing techniques for a given data set and develop their ensemble with optimized weights. In this paper we empirically evaluate the effectiveness of auto-sklearn in predicting the number of defects in software modules. In the experiment, we used software metrics of 20 OSS projects for cross-release defect prediction and compared auto-sklearn with random forest, decision tree and linear discriminant analysis by using Norm(Popt) as a performance measure. As a result, auto-sklearn showed similar prediction performance as random forest, which is one of the best prediction models for defect prediction in past studies. This indicates that auto-sklearn can obtain good prediction performance for defect prediction without any knowledge of machine learning techniques and models.

To avoid software development project failure, accurate estimation of software development effort is necessary at the beginning of a software project. This paper proposes to adjust the kurtosis and the skewness of project feature variables for better fitting of software estimation models. The proposed method conducts logarithmic transformation of variables, then conducts the kurtosis and skewness transformation to make the variable distribution closer to the normal distribution. To empirically evaluate the effectiveness of the proposed method, we employed three industry data sets and linear regression models with three-fold cross validation. The result of the evaluation showed that the models with the proposed method were better in both the goodness of fit and the estimation accuracy in terms of MMRE compared to log-log regression.

Joint attention, which is the ability of coordination of a common point of reference with the communicating party, emerges as a key factor in various interaction scenarios. This paper presents an image-based method for establishing joint attention between an experimenter and a robot. The precise analysis of the experimenter's eye region requires stability and high-resolution image acquisition, which is not always available. We investigate regression-based interpolation of the gaze direction from the head pose of the experimenter, which is easier to track. Gaussian process regression and neural networks are contrasted to interpolate the gaze direction. Then, we combine gaze interpolation with image-based saliency to improve the target point estimates and test three different saliency schemes. We demonstrate the proposed method on a human-robot interaction scenario. Cross-subject evaluations, as well as experiments under adverse conditions (such as dimmed or artificial illumination or motion blur), show that our method generalizes well and achieves rapid gaze estimation for establishing joint attention.

This study focuses on joint motion patterns of humans that move together with other humans or objects. Since this scope embraces `group motion', which relates only humans, and expands its extent of interactions accounting for various auxiliary instruments such as walking aids or pushcarts, we term this collective motion pattern as `coherent' motion. Coherence is proposed to be characterized by the distance between the moving parties, the scalar product of their velocities and the scalar product of the velocity vector and the displacement vector. The contribution of this study lies in the formulation of coherence in terms of the listed features through explicit mathematical models. The models are developed in accordance with a large database recorded in an uncontrolled environment involving a total of more than 500 mobile entities. The performance of the proposed models is evaluated qualitatively by comparing them to the empirical data and quantitatively by employing log-likelihoods. Comparison to an earlier work indicates that the proposed models improve the identification of coherence quality significantly well.

This paper proposes a simple yet novel method for recognition of certain sorts of moving entities incorporating their shape and motion patterns. Although shape features have been commonly employed in object recognition, motion characteristics are in general not integrated to geometric models. In the interest of utilizing the motion attributes, the trajectories are investigated to extract the `coherence quality' of the entities. Besides, at every step a geometric shape model is adopted and the parameters defining the shape model are utilized in obtaining the prior probabilities of the entities being a member of a particular class of interest. The coherence quality is used to get the posterior probabilities through a Bayesian approach. The main contribution of this paper is the incorporation of coherence quality in identification of moving entities. The proposed method is tested against clutter and occlusion in an uncontrolled environment with patterns collected from over 500 entities. It is shown to yield a satisfactory performance rate of 92% over the entire dataset with significant generalization capabilities without any restrictions on the application setting and with considerable occlusion and clutter.

Head pose and eye location estimation are two closely related issues which refer to similar application areas. In recent years, these problems have been studied individually in numerous works in the literature. Previous research shows that cylindrical head models and isophote based schemes provide satisfactory precision in head pose and eye location estimation, respectively. However, the eye locator is not adequate to accurately locate eye in the presence of extreme head poses. Therefore, head pose cues may be suited to enhance the accuracy of eye localization in the presence of severe head poses. In this paper, a hybrid scheme is proposed in which the transformation matrix obtained from the head pose is used to normalize the eye regions and, in turn the transformation matrix generated by the found eye location is used to correct the pose estimation procedure. The scheme is designed to (1) enhance the accuracy of eye location estimations in low resolution videos, (2) to extend the operating range of the eye locator and (3) to improve the accuracy and re-initialization capabilities of the pose tracker. From the experimental results it can be derived that the proposed unified scheme improves the accuracy of eye estimations by 16% to 23%. Further, it considerably extends its operating range by more than 15°, by overcoming the problems introduced by extreme head poses. Finally, the accuracy of the head pose tracker is improved by 12% to 24%.