Mining frequent bug-fix code changes

Detecting bugs as early as possible plays an important role in ensuring software quality before shipping. We argue that mining previous bug fixes can produce good knowledge about why bugs happen and how they are fixed. In this paper, we mine the change history of 717 open source projects to extract bug-fix patterns. We also manually inspect many of the bugs we found to get insights into the contexts and reasons behind those bugs. For instance, we found out that missing null checks and missing initializations are very recurrent and we believe that they can be automatically detected and fixed.

Articulated Statistical Shape Model-Based 2D-3D Reconstruction of a Hip Joint

In this paper, reconstruction of three-dimensional (3D) patient-specific models of a hip joint from two-dimensional (2D) calibrated X-ray images is addressed. Existing 2D-3D reconstruction techniques usually reconstruct a patient-specific model of a single anatomical structure without considering the relationship to its neighboring structures. Thus, when those techniques would be applied to reconstruction of patient-specific models of a hip joint, the reconstructed models may penetrate each other due to narrowness of the hip joint space and hence do not represent a true hip joint of the patient. To address this problem we propose a novel 2D-3D reconstruction framework using an articulated statistical shape model (aSSM). Different from previous work on constructing an aSSM, where the joint posture is modeled as articulation in a training set via statistical analysis, here it is modeled as a parametrized rotation of the femur around the joint center. The exact rotation of the hip joint as well as the patient-specific models of the joint structures, i.e., the proximal femur and the pelvis, are then estimated by optimally fitting the aSSM to a limited number of calibrated X-ray images. Taking models segmented from CT data as the ground truth, we conducted validation experiments on both plastic and cadaveric bones. Qualitatively, the experimental results demonstrated that the proposed 2D-3D reconstruction framework preserved the hip joint structure and no model penetration was found. Quantitatively, average reconstruction errors of 1.9 mm and 1.1 mm were found for the pelvis and the proximal femur, respectively.

3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.

Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking

Attractive business cases in various application fields contribute to the sustained long-term interest in indoor localization and tracking by the research community. Location tracking is generally treated as a dynamic state estimation problem, consisting of two steps: (i) location estimation through measurement, and (ii) location prediction. For the estimation step, one of the most efficient and low-cost solutions is Received Signal Strength (RSS)-based ranging. However, various challenges - unrealistic propagation model, non-line of sight (NLOS), and multipath propagation - are yet to be addressed. Particle filters are a popular choice for dealing with the inherent non-linearities in both location measurements and motion dynamics. While such filters have been successfully applied to accurate, time-based ranging measurements, dealing with the more error-prone RSS based ranging is still challenging. In this work, we address the above issues with a novel, weighted likelihood, bootstrap particle filter for tracking via RSS-based ranging. Our filter weights the individual likelihoods from different anchor nodes exponentially, according to the ranging estimation. We also employ an improved propagation model for more accurate RSS-based ranging, which we suggested in recent work. We implemented and tested our algorithm in a passive localization system with IEEE 802.15.4 signals, showing that our proposed solution largely outperforms a traditional bootstrap particle filter.

Modal interpolation via nested sequents

The main method of proving the Craig Interpolation Property (CIP) constructively uses cut-free sequent proof systems. Until now, however, no such method has been known for proving the CIP using more general sequent-like proof formalisms, such as hypersequents, nested sequents, and labelled sequents. In this paper, we start closing this gap by presenting an algorithm for proving the CIP for modal logics by induction on a nested-sequent derivation. This algorithm is applied to all the logics of the so-called modal cube.

Intuitionistic common knowledge or belief

Starting off from the usual language of modal logic for multi-agent systems dealing with the agents’ knowledge/belief and common knowledge/belief we define so-called epistemic Kripke structures for intu- itionistic (common) knowledge/belief. Then we introduce corresponding deductive systems and show that they are sound and complete with respect to these semantics.

SCAD: Sensor Context-aware Adaptive Duty-cycled Beaconless Opportunistic Routing for WSNs

Low quality of wireless links leads to perpetual transmission failures in lossy wireless environments. To mitigate this problem, opportunistic routing (OR) has been proposed to improve the throughput of wireless multihop ad-hoc networks by taking advantage of the broadcast nature of wireless channels. However, OR can not be directly applied to wireless sensor networks (WSNs) due to some intrinsic design features of WSNs. In this paper, we present a new OR solution for WSNs with suitable adaptations to their characteristics. Our protocol, called SCAD-Sensor Context-aware Adaptive Duty-cycled beaconless opportunistic routing protocol is a cross-layer routing approach and it selects packet forwarders based on multiple sensor context information. To reach a balance between performance and energy-efficiency, SCAD adapts the duty-cycles of sensors according to real-time traffic loads and energy drain rates. We compare SCAD against other protocols through extensive simulations. Evaluation results show that SCAD outperforms other protocols in highly dynamic scenarios.

Evaluation of Video Transmission Energy Consumption and Quality

The widespread use of wireless enabled devices and the increasing capabilities of wireless technologies has promoted multimedia content access and sharing among users. However, the quality perceived by the users still depends on multiple factors such as video characteristics, device capabilities, and link quality. While video characteristics include the video time and spatial complexity as well as the coding complexity, one of the most important device characteristics is the battery lifetime. There is the need to assess how these aspects interact and how they impact the overall user satisfaction. This paper advances previous works by proposing and validating a flexible framework, named EViTEQ, to be applied in real testbeds to satisfy the requirements of performance assessment. EViTEQ is able to measure network interface energy consumption with high precision, while being completely technology independent and assessing the application level quality of experience. The results obtained in the testbed show the relevance of combined multi-criteria measurement approaches, leading to superior end-user satisfaction perception evaluation .

Context Aware e-Learning Objects and their Types from the Perspective of the Object-Oriented Paradigm

The contribution of this article demonstrates how to identify context-aware types of e-Learning objects (eLOs) derived from the subject domains. This perspective is taken from an engineering point of view and is applied during requirements elicitation and analysis relating to present work in constructing an object-oriented (OO), dynamic, and adaptive model to build and deliver packaged e-Learning courses. Consequently, three preliminary subject domains are presented and, as a result, three primitive types of eLOs are posited. These types educed from the subject domains are of structural, conceptual, and granular nature. Structural objects are responsible for the course itself, conceptual objects incorporate adaptive and logical interoperability, while granular objects congregate granular assets. Their differences, interrelationships, and responsibilities are discussed. A major design challenge relates to adaptive behaviour. Future research addresses refinement on the subject domains and adaptive hypermedia systems.

Modularity and Integrity Issues in Instructional Engineering

Course materials for e-learning are a special type of information system (IS). Thus, in the development of educational material one may learn from principles, methods, and tools that originated in the Software Engineering (SE) discipline and that are relevant in similar ways in "Instructional Engineering". An important SE principle is mo dularization, which supports properties like reusability and adaptability of code. To foster the adaptability of courseware we present a concept in which learning material is organized as a library of modular course objects. A certain lecturer may customize the courseware according to his specific course requirements. He must consider logical dependencies of and relationship integrity between selected course objects. We discuss integrity issues that have to be regarded for the composition of consistent course materials.

Deep Points Consolidation

In this paper, we present a consolidation method that is based on a new representation of 3D point sets. The key idea is to augment each surface point into a deep point by associating it with an inner point that resides on the meso-skeleton, which consists of a mixture of skeletal curves and sheets. The deep points representation is a result of a joint optimization applied to both ends of the deep points. The optimization objective is to fairly distribute the end points across the surface and the meso-skeleton, such that the deep point orientations agree with the surface normals. The optimization converges where the inner points form a coherent meso-skeleton, and the surface points are consolidated with the missing regions completed. The strength of this new representation stems from the fact that it is comprised of both local and non-local geometric information. We demonstrate the advantages of the deep points consolidation technique by employing it to consolidate and complete noisy point-sampled geometry with large missing parts.

A Unified Approach to Automatic Testing of Architectural Constraints

Architectural decisions are often encoded in the form of constraints and guidelines. Non-functional requirements can be ensured by checking the conformance of the implementation against this kind of invariant. Conformance checking is often a costly and error-prone process that involves the use of multiple tools, differing in effectiveness, complexity and scope of applicability. To reduce the overall effort entailed by this activity, we propose a novel approach that supports verification of human- readable declarative rules through the use of adapted off-the-shelf tools. Our approach consists of a rule specification DSL, called Dicto, and a tool coordination framework, called Probo. The approach has been implemented in a soon to be evaluated prototype.

Towards moldable development tools

Developers commonly ask detailed and domain-specific questions about the software systems they are developing and maintaining. Integrated development environments (IDEs) form an essential category of tools for developing software that should support software engineering decision making. Unfortunately, rigid and generic IDEs that focus on low-level programming tasks, that promote code rather than data, and that suppress customization, offer limited support for informed decision making during software development. We propose to improve decision making within IDEs by moving from generic to context-aware IDEs through moldable tools. In this paper, we promote the idea of moldable tools, illustrate it with concrete examples, and discuss future research directions.

Optimized CUDA-Based PDE Solver for Reaction Diffusion Systems on Arbitrary Surfaces

Partial differential equation (PDE) solvers are commonly employed to study and characterize the parameter space for reaction-diffusion (RD) systems while investigating biological pattern formation. Increasingly, biologists wish to perform such studies with arbitrary surfaces representing ‘real’ 3D geometries for better insights. In this paper, we present a highly optimized CUDA-based solver for RD equations on triangulated meshes in 3D. We demonstrate our solver using a chemotactic model that can be used to study snakeskin pigmentation, for example. We employ a finite element based approach to perform explicit Euler time integrations. We compare our approach to a naive GPU implementation and provide an in-depth performance analysis, demonstrating the significant speedup afforded by our optimizations. The optimization strategies that we exploit could be generalized to other mesh based processing applications with PDE simulations.