Sean Banerjee

Eclipse vs. Mozilla: A Comparison of Two Large-Scale Open Source Problem Report Repositories

Bug tracking systems play an important role in the development and maintenance of large-scale software systems. Having access to open source bug tracking systems has allowed researchers to take advantage of rich datasets and propose solutions to manage duplicate report classification, developer assignment and quality assessment. In spite of research advances, our understanding of the content of these repositories remains limited, primarily because of their size. In many cases, researchers analyze small portions of datasets thus limiting the understanding of the dynamics of problem reporting. The objective of this study is to explore the properties of two large-scale open source problem report repositories. The Eclipse dataset, at the time of download, consisted of 363; 770 reports spanning 11+ years, whereas Mozilla contained 699; 085 reports spanning 14+ years.Our research examines the evolution of datasets over time by analyzing the changes in the repository and the profiles of users who submit problem reports. We provide quantitative evidence on how submitters maturity reduces the propensity to submit poor quality, insignificant or duplicate reports. We show that a diverse user base, characteristic of Mozilla, creates challenges for the development team as they spend more time triaging, rather than fixing, issues. Finally, we provide the research community with a series of observations and suggestions on how to study large-scale problem repositories.

Effect of User Posture and Device Size on the Performance of Touch-Based Authentication Systems

Touch dynamics is a behavioral biometric that authenticates users by analyzing the characteristics of the touch gestures they execute on devices such as tablets and smartphones. The current research in this field has focused on identifying the best algorithms and the most effective attributes to improve authentication performance. However, a robust touch dynamics based authentication system for mobile devices must also be resilient against environmental variables such as user posture, movement, device size, device manufacturer, etc. In this work, we focus on two critical environmental variables that affect touch based authentication systems. We demonstrate that the users posture and device size have a significant impact on the performance of touch based authentication systems. Our results indicate that authentication performance is proportional to the device size. Furthermore, we conclude that using a devices 3-D orientation is necessary to attain better authentication performance. Our findings indicate that the features used in state-of-the-art touch-based authentication systems are insufficient to provide constant, reliable performance when either the device size or user posture change. The effect of environmental variables on touch dynamics has not been explored. The results presented in this work are the first of its kind and important in the development of robust touch-based authentication systems. This study has immediate, applicable benefits to develop better authentication approaches touch dynamics.

Automated triaging of very large bug repositories

Abstract Context: Bug tracking systems play an important role in software maintenance. They allow both developers and users to submit problem reports on observed failures. However, by allowing anyone to submit problem reports, it is likely that more than one reporter will report on the same issue. Research in open source repositories has focused on two broad areas: determining the original report associated with each known duplicate, and assigning a developer to fix a particular problem. Objective: Limited research has been done in developing a fully automated triager, one that can first ascertain if a problem report is original or duplicate, and then provide a list of 20 potential matches for a duplicate report. We address this limitation by developing an automated triaging system that can be used to assist human triagers in bug tracking systems. Method: Our automated triaging system automatically assigns a label of original or duplicate to each incoming problem report, and provides a list of 20 suggestions for reports classified as duplicate. The system uses 24 document similarity measures and associated summary statistics, along with a suite of document property and user metrics. We perform our research on a lifetime of problem reports from the Eclipse, Firefox and Open Office repositories. Results: Our system can be used as a filtration aide, with high original recall exceeding 95% and low duplicate recall, or as a triaging guide, with balanced recall of approximately 70% for both originals and duplicates. Furthermore, the system reduces the workload on the triager by over 90%. Conclusions: Our work represents the first full scale effort at automatically triaging problem reports in open source repositories. By utilizing multiple similarity measures, we reduce the potential of false matches caused by the diversity of human language.

Empirical Techniques to Detect and Mitigate the Effects of Irrevocably Evolving User Profiles in Touch-Based Authentication Systems

Touch dynamics (or touch based authentication) refers to a behavioral biometric for touchscreen devices wherein a user is authenticated based on his/her executed touch gestures. In this work, we present the results of a series of empirical techniques to detect habituation in the users touch profile, its detrimental effect on authentication accuracy and strategies to overcome these effects. Habituation here refers to changes in the users profile and/or noise within it due to the users familiarization with the device and software application. The results of this work show that habituation causes the users touch profile to evolve significantly and irrevocably over time even after the user is familiar with the device and software application. This phenomenon considerably degrades classifier accuracy. We show that this effect can be best mitigated using approximately 300 most recent user inputs and retraining the classifier. The retrained classifier can be used with minimal increase in error rate on up to 75 new user inputs. This results in an error rate of 3.68% that sets the benchmark in this field for a realistic test setup. Finally, we quantify the benefits of vote-based reclassification of predicted class labels and show that this technique is vital for achieving high accuracy in realistic touch-based authentication systems.

Multi-camera Microenvironment to Capture Multi-view Time-Lapse Videos for 3D Analysis of Aging Objects

We present a microenvironment of multiple cameras to capture multi-viewpoint time-lapse videos of objects showing spatiotemporal phenomena such as aging. Our microenvironment consists of four synchronized Raspberry Pi v2 cameras triggered by four corresponding Raspberry Pi v3 computers that are controlled by a central computer. We provide a graphical user interface for users to trigger captures and visualize multiple viewpoint videos. We show multiple viewpoint captures for objects such as fruit that depict shape changes due to water volume loss and appearance changes due to enzymatic browning.

A Virtual Reality Interface for Interactions with Spatiotemporal 3D Data

Traditional interfaces for interacting with 3D models in virtual environments lack support for spatiotemporal 3D models such as point clouds and meshes generated by markerless capture systems. We present a virtual reality (VR) interface that enables the user to perform spatial and temporal interactions with spatiotemporal 3D models. To accommodate the high volume of spatiotemporal data, we provide a data format for spatiotemporal 3D models which has an average speedup of 3.84 and a space reduction of 43.9% over traditional model file formats. We enable the user to manipulate spatiotemporal 3D data using gestures intuitive from real-world experience or by using a VR user interface similar to traditional 2D visual interactions.

Programmatic 3D Printing of a Revolving Camera Track to Automatically Capture Dense Images for 3D Scanning of Objects

Low-cost 3D scanners and automatic photogrammetry software have brought digitization of objects into 3D models to the level of the consumer. However, the digitization techniques are either tedious, disruptive to the scanned object, or expensive. We create a novel 3D scanning system using consumer grade hardware that revolves a camera around the object of interest. Our approach does not disturb the object during capture and allows us to scan delicate objects that can deform under motion, such as potted plants. Our system consists of a Raspberry Pi camera and computer, stepper motor, 3D printed camera track, and control software. Our 3D scanner allows the user to gather image sets for 3D model reconstruction using photogrammetry software with minimal effort. We scale 3D scanning to objects of varying sizes by designing our scanner using programmatic modeling, and allowing the user to change the physical dimensions of the scanner without redrawing each part.

GPU Acceleration of Document Similarity Measures for Automated Bug Triaging

Large-scale open source software bug repositories from companies such as Mozilla, RedHat, Novell and Eclipse have enabled researchers to develop automated solutions to bug triaging problems such as bug classification, duplicate classification and developer assignment. However, despite the repositories containing millions of usable reports, researchers utilize only a small fraction of the data. A major reason for this is the polynomial time and cost associated with making comparisons to all prior reports. Graphics processing units (GPUs) with several thousand cores have been used to accelerate algorithms in several domains, such as computer graphics, computer vision and linguistics. However, they have remained unexplored in the area of bug triaging. In this paper, we demonstrate that the problem of comparing a bug report to all prior reports is an embarassingly parallel problem, that can be accelerated using graphics processing unit (GPUs). Comparing the similarity of two bug reports can be performed using frequency based methods (e.g. cosine similarity and BM25F), sequence based methods (e.g. longest common substring and longest common subsequence) or topic modeling. For the purpose of this paper we focus on cosine similarity, longest common substring and longest common subsequence. Using an NVIDIA Tesla K40 GPU, we show that frequency and sequence based similarity measures are accelerated by 89 and 85 times respectively when compared to a pure CPU based implementation. Thus, allowing us to generate similarity scores for the entire Eclipse repository, consisting of 498,161 reports in under a day, as opposed to 83.4 days using a CPU based approach.

Water Fixture Identification in Smart Housing: A Domain Knowledge Based Case Study

In current practice, smart housing environments often over-install smart sensors on every fixture and log data from them at high sampling rates, resulting in more data being collected than is necessary. Fixture identification offers a possible alternative to reduce the number of sensors installed and the amount of data collected in smart housing. Fixture identification applies classifiers to label utility consumption data aggregated at the apartment level by the specific fixture that actually contributes the data, such as the shower or the kitchen sink. Successful fixture identification can be used to educate tenants, optimize the resource supply strategy, and offer a smart solution for detecting abnormal usage activities. In this paper, we report a case study of water fixture identification by using support vector machines (SVMs) to perform fixture classification. We use the Smart Housing Dataset from Clarkson University, which comprises of one academic year of tenant activities from 12 student apartments. Our results show that the proposed approach achieves an average accuracy between 78% to 87.8% for identifying hot water fixtures including kitchen sink, bathroom sink and shower. As a result, the number of smart meters per apartment is reduced from 7 to 3, one for hot water, one for cold water, and the third for toilet. The novelty of our study lies in the feature selection process, which is guided by our domain knowledge of water fixture characteristics and the correlation between water fixture usage and other user behavior in the apartments. We describe our proposed features, their rationale, and their effect on classification performance.

Spatiotemporal 3D Models of Aging Fruit from Multi-view Time-Lapse Videos.

We provide an approach to reconstruct spatiotemporal 3D models of aging objects such as fruit containing time-varying shape and appearance using multi-view time-lapse videos captured by a microenvironment of Raspberry Pi cameras. Our approach represents the 3D structure of the object prior to aging using a static 3D mesh reconstructed from multiple photographs of the object captured using a rotating camera track. We manually align the 3D mesh to the images at the first time instant. Our approach automatically deforms the aligned 3D mesh to match the object across the multi-viewpoint time-lapse videos. We texture map the deformed 3D meshes with intensities from the frames at each time instant to create the spatiotemporal 3D model of the object. Our results reveal the time dependence of volume loss due to transpiration and color transformation due to enzymatic browning on banana peels and in exposed parts of bitten fruit.