Studying high impact fix-inducing changes

As software systems continue to play an important role in our daily lives, their quality is of paramount importance. Therefore, a plethora of prior research has focused on predicting components of software that are defect-prone. One aspect of this research focuses on predicting software changes that are fix-inducing. Although the prior research on fix-inducing changes has many advantages in terms of highly accurate results, it has one main drawback: It gives the same level of impact to all fix-inducing changes. We argue that treating all fix-inducing changes the same is not ideal, since a small typo in a change is easier to address by a developer than a thread synchronization issue. Therefore, in this paper, we study high impact fix-inducing changes (HIFCs). Since the impact of a change can be measured in different ways, we first propose a measure of impact of the fix-inducing changes, which takes into account the implementation work that needs to be done by developers in later (fixing) changes. Our measure of impact for a fix-inducing change uses the amount of churn, the number of files and the number of subsystems modified by developers during an associated fix of the fix-inducing change. We perform our study using six large open source projects to build specialized models that identify HIFCs, determine the best indicators of HIFCs and examine the benefits of prioritizing HIFCs. Using change factors, we are able to predict 56 % to 77 % of HIFCs with an average false alarm (misclassification) rate of 16 %. We find that the lines of code added, the number of developers who worked on a change, and the number of prior modifications on the files modified during a change are the best indicators of HIFCs. Lastly, we observe that a specialized model for HIFCs can provide inspection effort savings of 4 % over the state-of-the-art models. We believe our results would help practitioners prioritize their efforts towards the most impactful fix-inducing changes and save inspection effort.     

Drivers of mobile payment acceptance: The impact of network externalities

Mobile payment is an attractive option that has recently boomed because of the advent of smart phones and their applications. Despite the great potential of such technology in simplifying our lives, its uptake remains limited. As the technology acceptance fails to meet expectations, this study aims at providing a better understanding of the factors influencing mobile payment acceptance. Through an empirical investigation that couples the traditional technology acceptance factors with “network externalities” effect. This study hypothesized that performance expectancy, effort expectancy; social influence, trust, and network externality are major factors that influence the intention to use mobile payment. Results indicated that while the traditional acceptance drivers still impact customers’ willingness to adopt mobile payment, network externalities was the most influential driver of mobile payment acceptance. Results also failed to support the influence of effort expectancy. Conclusions and future work propositions are stated at the end.     

A constant-force bistable mechanism for force regulation and overload protection

A novel constant-force bistable mechanism (CFBM) allowing constant contact force and overload protection is developed. When a device equipped with a CFBM is loaded by an unknown force exceeding a critical force of the CFBM, the CFBM can snap to its other stable equilibrium state to safeguard the device. The bistability of the mechanism originates from combined compression and bending of the beam structures. Finite element analyses are used to characterize the constant-force behavior and bistability of the mechanism under static loading. A design formulation is proposed to find the CFBM shape for a specified displacement range with constant output force of the mechanism. Prototypes of the CFBM are fabricated and tested. The characteristics of the CFBM predicted by theory are verified by experiments. Using the CFBM, sophisticated sensors and control system for force regulation of machining systems can be eliminated.     

Studying re-opened bugs in open source software

Bug fixing accounts for a large amount of the software maintenance resources. Generally, bugs are reported, fixed, verified and closed. However, in some cases bugs have to be re-opened. Re-opened bugs increase maintenance costs, degrade the overall user-perceived quality of the software and lead to unnecessary rework by busy practitioners. In this paper, we study and predict re-opened bugs through a case study on three large open source projects—namely Eclipse, Apache and OpenOffice. We structure our study along four dimensions: (1) the work habits dimension (e.g., the weekday on which the bug was initially closed), (2) the bug report dimension (e.g., the component in which the bug was found) (3) the bug fix dimension (e.g., the amount of time it took to perform the initial fix) and (4) the team dimension (e.g., the experience of the bug fixer). We build decision trees using the aforementioned factors that aim to predict re-opened bugs. We perform top node analysis to determine which factors are the most important indicators of whether or not a bug will be re-opened. Our study shows that the comment text and last status of the bug when it is initially closed are the most important factors related to whether or not a bug will be re-opened. Using a combination of these dimensions, we can build explainable prediction models that can achieve a precision between 52.1–78.6 % and a recall in the range of 70.5–94.1 % when predicting whether a bug will be re-opened. We find that the factors that best indicate which bugs might be re-opened vary based on the project. The comment text is the most important factor for the Eclipse and OpenOffice projects, while the last status is the most important one for Apache. These factors should be closely examined in order to reduce maintenance cost due to re-opened bugs.     

Multi-mode of vortex-induced vibration of a flexible circular cylinder

The vortex-induced vibration of a flexible circular cylinder is investigated at a constant Reynolds number of 1 000.The finite-volume method on moving meshes is applied for the fluid flow, and the Euler-Bernoulli beam theory is used to model the dynamic response of a flexible cylinder.The relationship between the reduced velocity and the amplitude response agrees well with the experimental results.Moreover, five different vibrating modes appear in the simulation.From the comparisons of their vortex structures, the strength of the wake flow is related to the exciting vibrating mode and different vortex patterns arise for different vibrating modes.Only 2P pattern appears in the first vibrating mode while 2S-2P patterns occur in the other vibrating modes if monitoring at different sections along the length of the cylinder.The vibration of the flexible cylinder can also greatly alter the three-dimensionality in the wake, which needs further studies in our future work, especially in the transition region for the Reynolds number from 170 to 300.     

Dual watermark for image tamper detection and recovery

An effective dual watermark scheme for image tamper detection and recovery is proposed in this paper. In our algorithm, each block in the image contains watermark of other two blocks. That is to say, there are two copies of watermark for each non-overlapping block in the image. Therefore, we maintain two copies of watermark of the whole image and provide second chance for block recovery in case one copy is destroyed. A secret key, which is transmitted along with the watermarked image, and a public chaotic mixing algorithm are used to extract the watermark for tamper recovery. By using our algorithm, a 90% tampered image can be recovered to a dim yet still recognizable condition (PSNR ). Experimental results demonstrate that our algorithm is superior to the compared techniques, especially when the tampered area is large.     

Fast online graph clustering via Erd?s-Rényi mixture

In the context of graph clustering, we consider the problem of simultaneously estimating both the partition of the graph nodes and the parameters of an underlying mixture of affiliation networks. In numerous applications the rapid increase of data size over time makes classical clustering algorithms too slow because of the high computational cost. In such situations online clustering algorithms are an efficient alternative to classical batch algorithms. We present an original online algorithm for graph clustering based on a Erd?s-Rényi graph mixture. The relevance of the algorithm is illustrated, using both simulated and real data sets. The real data set is a network extracted from the French political blogosphere and presents an interesting community organization.     

Who is LB1? Discriminant analysis for the classification of specimens

Many problems in paleontology reduce to finding those features that best discriminate among a set of classes. A clear example is the classification of new specimens. However, these classifications are generally challenging because the number of discriminant features and the number of samples are limited. This has been the fate of LB1, a new specimen found in the Liang Bua Cave of Flores. Several authors have attributed LB1 to a new species of Homo, H. floresiensis. According to this hypothesis, LB1 is either a member of the early Homo group or a descendent of an ancestor of the Asian H. erectus. Detractors have put forward an alternate hypothesis, which stipulates that LB1 is in fact a microcephalic modern human. In this paper, we show how we can employ a new Bayes optimal discriminant feature extraction technique to help resolve this type of issues. In this process, we present three types of experiments. First, we use this Bayes optimal discriminant technique to develop a model of morphological (shape) evolution from Australopiths to H. sapiens. LB1 fits perfectly in this model as a member of the early Homo group. Second, we build a classifier based on the available cranial and mandibular data appropriately normalized for size and volume. Again, LB1 is most similar to early Homo. Third, we build a brain endocast classifier to show that LB1 is not within the normal range of variation in H. sapiens. These results combined support the hypothesis of a very early shared ancestor for LB1 and H. erectus, and illustrate how discriminant analysis approaches can be successfully used to help classify newly discovered specimens.     

Type-2 fuzzy Gaussian mixture models

This paper presents a new extension of Gaussian mixture models (GMMs) based on type-2 fuzzy sets (T2 FSs) referred to as T2 FGMMs. The estimated parameters of the GMM may not accurately reflect the underlying distributions of the observations because of insufficient and noisy data in real-world problems. By three-dimensional membership functions of T2 FSs, T2 FGMMs use footprint of uncertainty (FOU) as well as interval secondary membership functions to handle GMMs uncertain mean vector or uncertain covariance matrix, and thus GMMs parameters vary anywhere in an interval with uniform possibilities. As a result, the likelihood of the T2 FGMM becomes an interval rather than a precise real number to account for GMMs uncertainty. These interval likelihoods are then processed by the generalized linear model (GLM) for classification decision-making. In this paper we focus on the role of the FOU in pattern classification. Multi-category classification on different data sets from UCI repository shows that T2 FGMMs are consistently as good as or better than GMMs in case of insufficient training data, and are also insensitive to different areas of the FOU. Based on T2 FGMMs, we extend hidden Markov models (HMMs) to type-2 fuzzy HMMs (T2 FHMMs). Phoneme classification in the babble noise shows that T2 FHMMs outperform classical HMMs in terms of the robustness and classification rate. We also find that the larger area of the FOU in T2 FHMMs with uncertain mean vectors performs better in classification when the signal-to-noise ratio is lower.     

Piecewise affine kernel tracking for non-planar targets

Since the multiple kernel representation opened in tracking the possibility of representing several features of the target in the same model, tracking multiple features using kernel-based methods has received a great attention. In spite of these efforts, the formulation has been reduced to tracking planar targets or targets rotating inside a plane parallel to the image plane. The aim of this paper is to extend the multi-kernel tracking to cope with situations different to those. To this end, we consider the triangular mesh described by the centers of the kernels and we develop the estimation of a set of affine transforms, one at each mesh triangle, subject to the constraints that each affine transform of a triangle must be compatible with the affine transforms coming from contiguous triangles. The method is applied to sequences including face and car tracking. Results show an outperformance respect to previous kernel tracking methods, which generally work with a too restricted set of movements.