An industry experiment on the effects of test-driven development on external quality and productivity

Existing empirical studies on test-driven development (TDD) report different conclusions about its effects on quality and productivity. Very few of those studies are experiments conducted with software professionals in industry. We aim to analyse the effects of TDD on the external quality of the work done and the productivity of developers in an industrial setting. We conducted an experiment with 24 professionals from three different sites of a software organization. We chose a repeated-measures design, and asked subjects to implement TDD and incremental test last development (ITLD) in two simple tasks and a realistic application close to real-life complexity. To analyse our findings, we applied a repeated-measures general linear model procedure and a linear mixed effects procedure. We did not observe a statistical difference between the quality of the work done by subjects in both treatments. We observed that the subjects are more productive when they implement TDD on a simple task compared to ITLD, but the productivity drops significantly when applying TDD to a complex brownfield task. So, the task complexity significantly obscured the effect of TDD. Further evidence is necessary to conclude whether TDD is better or worse than ITLD in terms of external quality and productivity in an industrial setting. We found that experimental factors such as selection of tasks could dominate the findings in TDD studies.     

ZnS nanocrystals decorated single-walled carbon nanotube based chemiresistive label-free DNA sensor

We fabricated ZnS nanocrystals decorated single-walled carbon nanotube (SWNT) based chemiresistive sensor for DNA. Since the charge transfer in the hybrid nanostructures is considered to be responsible for many of their unique properties, the role of ZnS nanocrystals toward its performance in DNA sensor was delineated. It was found that the free carboxyl groups surrounding the ZnS nanocrystals allowed large loading of single strand DNA (ssDNA) probe that provided an ease of hybridization with target complementary c-ssDNA resulting in large electron transfer to SWNT. Thus it provided a significant improvement in sensitivity toward c-ssDNA as compared to bare SWNT based DNA sensor.     

The role of non-exact replications in software engineering experiments

In no science or engineering discipline does it make sense to speak of isolated experiments. The results of a single experiment cannot be viewed as representative of the underlying reality. Experiment replication is the repetition of an experiment to double-check its results. Multiple replications of an experiment increase the confidence in its results. Software engineering has tried its hand at the identical (exact) replication of experiments in the way of the natural sciences (physics, chemistry, etc.). After numerous attempts over the years, apart from experiments replicated by the same researchers at the same site, no exact replications have yet been achieved. One key reason for this is the complexity of the software development setting, which prevents the many experimental conditions from being identically reproduced. This paper reports research into whether non-exact replications can be of any use. We propose a process aimed at researchers running non-exact replications. Researchers enacting this process will be able to identify new variables that are possibly having an effect on experiment results. The process consists of four phases: replication definition and planning, replication operation and analysis, replication interpretation, and analysis of the replication’s contribution. To test the effectiveness of the proposed process, we have conducted a multiple-case study, revealing the variables learned from two different replications of an experiment.     

On the sliding mode control of MIMO nonlinear systems: An input‐output approach

We address the sliding mode control design problem for output reference trajectory tracking problems in the special class of MIMO flat systems known as static feedback linearizable systems. We assume unavailable system state components but rely on available inputs and measurable flat outputs. Each controller will largely ignore state and control input couplings by adopting a standard sliding mode controller scheme derived from the SISO case and used this as decoupled input‐to‐flat‐output model. The standard controller arises from a vastly simplified pure integration, additively perturbed, system. The simplified pure integration system controlled trajectories are shown to be time‐scale homotopically equivalent to those of the nonlinear flat system. The basic sliding surface coordinate function design is approached from the perspective of structural integral reconstructors requiring only the inputs and the flat outputs of the system. Integral structural reconstructors were introduced by Fliess et al for the control of linear SISO and MIMO systems, giving rise to the generalized proportional integral control method. Simulations are presented for SISO and MIMO systems and experimental results are reported for a two‐degree‐of‐freedom fully actuated robotic manipulator.     

Establishment of Human-Induced Pluripotent Stem Cell-Derived Neurons—A Promising In Vitro Model for a Molecular Study of Rabies Virus and Host Interaction

Rabies is a deadly viral disease caused by the rabies virus (RABV), transmitted through a bite of an infected host, resulting in irreversible neurological symptoms and a 100% fatality rate in humans. Despite many aspects describing rabies neuropathogenesis, numerous hypotheses remain unanswered and concealed. Observations obtained from infected primary neurons or mouse brain samples are more relevant to human clinical rabies than permissive cell lines; however, limitations regarding the ethical issue and sample accessibility become a hurdle for discovering new insights into virus–host interplays. To better understand RABV pathogenesis in humans, we generated human-induced pluripotent stem cell (hiPSC)-derived neurons to offer the opportunity for an inimitable study of RABV infection at a molecular level in a pathologically relevant cell type. This study describes the characteristics and detailed proteomic changes of hiPSC-derived neurons in response to RABV infection using LC-MS/MS quantitative analysis. Gene ontology (GO) enrichment of differentially expressed proteins (DEPs) reveals temporal changes of proteins related to metabolic process, immune response, neurotransmitter transport/synaptic vesicle cycle, cytoskeleton organization, and cell stress response, demonstrating fundamental underlying mechanisms of neuropathogenesis in a time-course dependence. Lastly, we highlighted plausible functions of heat shock cognate protein 70 (HSC70 or HSPA8) that might play a pivotal role in regulating RABV replication and pathogenesis. Our findings acquired from this hiPSC-derived neuron platform help to define novel cellular mechanisms during RABV infection, which could be applicable to further studies to widen views of RABV-host interaction.     

A cascode modulated class-E power amplifier for wireless communications

A cascode modulated CMOS class-E power amplifier (PA) is presented in this paper. It is shown that by applying a modulated signal to the gate of the cascode transistor the output power is modulated. The main advantage of the proposed technique is a high 35 dB output power dynamic range. The peak power added efficiency (PAE) is 35%. The concept of the cascode power control of class-E RF PA operating at 2.2 GHz with 18 dBm output power was implemented in a CMOS technology and the performance has been verified by measurements. The prototype CMOS PA is tested by single tone excitation and by enhanced data rates for GSM evolution (EDGE) modulated signal. Digital predistortion is used to linearize the transfer characteristic. The EDGE spectrum mask is met and the rms error vector magnitude (EVM) is less than 4° in the entire output power range.     

Stability of active disturbance rejection control for uncertain systems: A Lyapunov perspective

In this work, we introduce a simple stability analysis to justify, under some suitable assumptions, the active disturbance rejection control method, used in the feedback regulation of a substantially uncertain plant. A criterion is obtained that allows us to define under what conditions closed‐loop stability can be assured. When the plant is mostly unknown, the criterion allows us to guarantee exponential convergence for the output‐feedback regulation problem, in the presence of a constant external perturbation, and practical stability when the external perturbation and the tracking reference signal are both time‐varying. In the latter case, the confinement error can be made as small as desired. To carry out the corresponding stability analysis, we derive the tracking error equation, and the observation error equation. To decouple these error equations, we use the Sylvester equation. Finally, we applied the direct Lyapunov method to analyze the corresponding convergence of the observation error and of the tracking error. Copyright © 2017 John Wiley & Sons, Ltd.     

Modeling and correction of multipath interference in time of flight cameras

Multipath interference of light is the cause of important errors in Time of Flight (ToF) depth estimation. This paper proposes an algorithm that removes multipath distortion from a single depth map obtained by a ToF camera. Our approach does not require information about the scene, apart from ToF measurements. The method is based on fitting ToF measurements with a radiometric model. Model inputs are depth values free from multipath interference whereas model outputs consist of synthesized ToF measurements. We propose an iterative optimization algorithm that obtains model parameters that best reproduce ToF measurements, recovering the depth of the scene without distortion. We show results with both synthetic and real scenes captured by commercial ToF sensors. In all cases, our algorithm accurately corrects the multipath distortion, obtaining depth maps that are very close to ground truth data.     

Identification and tracking of robots in an intelligent space using static cameras and an XPFCP

This paper tackles the problem of identification and tracking of multiple robots in an intelligent space using an array of cameras placed in fixed positions within the environment. Several types of agent can be found in an intelligent space: controlled agents (mobile robots) and uncontrolled ones (users and obstacles). The information transferred between the controlled agents and the intelligent space is limited to the control commands sent to the robots and the measurements of the odometers received from the robots. The proposed solution allows the localization of mobile agents, even if they are not robots; however, we have focused on the controlled agents. The proposal does not require prior knowledge or invasive landmarks on board the robots. It starts from the segmentation of different agents in motion that allows obtaining the boundaries of all robots and an estimation of all 3D points that define those boundaries. Then, the identification of the robots is obtained by comparing the components of the linear velocity estimated by the motion segmentation algorithm and received from the odometers. In order to track the robots, an eXtended Particle Filter with Classification Process (XPFCP) is employed. Several experimental tests have been carried out, and the results obtained validate the proposal.