Software Performance Research Articles

Transit Agency Assessment of Their Capability to Adopt New Mobility Strategies

This research identified current challenges, constraints and interest in the implementation and management of new mobility services (on-demand and micromobility) for United States transit agencies. Twenty-four semi-structured interviews with employees of transit agencies were conducted, and NVivo, a qualitative analysis software, was used to classify agency involvement, constraints, and challenges with services. The most common constraints and challenges for on-demand services (cited by more than five agencies) were labor shortages, software performance, fare integration, vehicle shortages, and funding. For agencies not offering on-demand services, developing strong backbone networks with fixed-routes was a priority, and cost-effectiveness, service area design, and funding were additional concerns. The top constraints and challenges among transit agencies with respect to micromobility (cited by more than four agencies) included fare integration, obstruction of public space, funding, adequate supporting infrastructure, and development of equitable service. For integrating new mobility services, most transit agencies (14 of 20, 70%) expressed interest in becoming mobility managers, but larger agencies (those with more than 10 million unlinked passenger trips) were split between mobility manager and partner roles. This research suggests that transit agencies have evolved in the new mobility space, but some constraints and challenges still stand in their way. Policy makers and decision makers can help transit agencies by encouraging open-source software development, improving labor contracts, increasing funding opportunities, and coordinating land-use, transportation, and transit planning. Future research should continue investigating barriers to entry for new mobility services and identify how transit agencies can increase coordination with technology and micromobility companies.

Performance Bug Analysis and Detection for Distributed Storage and Computing Systems

This article systematically studies 99 distributed performance bugs from five widely deployed distributed storage and computing systems (Cassandra, HBase, HDFS, Hadoop MapReduce and ZooKeeper). We present the TaxPerf database, which collectively organizes the analysis results as over 400 classification labels and over 2,500 lines of bug re-description. TaxPerf is classified into six bug categories (and 18 bug subcategories) by their root causes; resource, blocking, synchronization, optimization, configuration, and logic. TaxPerf can be used as a benchmark for performance bug studies and debug tool designs. Although it is impractical to automatically detect all categories of performance bugs in TaxPerf, we find that an important category of blocking bugs can be effectively solved by analysis tools. We analyze the cascading nature of blocking bugs and design an automatic detection tool called PCatch , which (i) performs program analysis to identify code regions whose execution time can potentially increase dramatically with the workload size; (ii) adapts the traditional happens-before model to reason about software resource contention and performance dependency relationship; and (iii) uses dynamic tracking to identify whether the slowdown propagation is contained in one job. Evaluation shows that PCatch can accurately detect blocking bugs of representative distributed storage and computing systems by observing system executions under small-scale workloads.

Emoticontrol: Emotions-based Control of User-Interfaces Adaptations

Emotions are integral to human nature, and their existence, duration, and evolution could lead to specific behaviors. If emotions and behaviors are ignored in the design of socio-technical systems, they will fail or cause discomfort. User interfaces (UIs) are elements of interactive systems able to trigger or moderate emotions. UIs are increasingly designed adaptive to users' various characteristics, intending to improve their satisfaction, performance, and decisions. However, previous adaptation supervising approaches are not effectively adopted in real life since they neglect the dynamic behaviors of humans or systems. This paper proposes Emoticontrol, a quality-driven approach to adapting UIs to users' emotions using Model-Free Reinforcement Learning (MFRL). The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users' enhanced quality of experience (QoE). The approach also considers improving the software quality of service (QoS) by designing software architecture alternatives. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in evacuation training. By taking contextual input of the users' basic emotions from face recognition, the application intelligently adapts its UI to quickly lead people to safe areas while keeping them emotionally controlled. We consider software performance a crucial QoS; thus, we adopt and test architectures that facilitate an acceptable level of performance. The evaluation process confirms the efficiency and effectiveness of the MFRL in iterations, as well as compared to other UI adaptation techniques.

Quantifying the relationship between software design principles and performance in Jason: a case study with simulated mobile robots

Quantifying the relationship between software design principles and performance in Jason: a case study with simulated mobile robots

Application of metamorphic testing on UAV path planning software

Both the performance and reliability evaluation processes of the unmanned aerial vehicle path planning software rely on the determination of the correctness of the execution results of unmanned aerial vehicle path planning software. However, this task is hindered due to the testing oracle problem. In this paper, a framework is designed to overcome the oracle problem and verify the correctness of path planning software based on the grid searching algorithms. In this framework, a metamorphic testing-based method is proposed, and three operations-based metamorphic relations are proposed and proved towards the target software. While analysis of the software is conducted, the version with manually injected faults as well as the officially released version are both dealt with. It is shown that in the experimental results the injected faults can be effectively revealed by using the methods proposed in this paper. Besides, through the evaluation of different types of metamorphic relations, we find that the composed metamorphic relations have stronger fault detection capability compared to the individual ones.

Accuracy evaluation of UAS photogrammetry and structure from motion in 3D modeling and volumetric calculations

Photogrammetric surveying using unmanned aerial systems (UAS) and structure from motion (SfM) is a rapidly emerging technique that has been widely used as a valuable research tool in geodesy and related disciplines. We studied surveying and three-dimensional (3D) modeling based on UAS and SfM. In particular, we focus on the accuracy and quality of UAS-SfM in 3D modeling and volumetric calculation of a building with an overhanging feature. For this purpose, two aerial surveys were performed using a fixed-wing and a multirotor aircraft with nadir and oblique camera axes. For validating the results, field surveys were carried out using terrestrial laser scanner (TLS), differential global navigation satellite system, and total station. To study the performance of SfM software on the accuracy and quality of point clouds, UAS images were processed using three software packages: Agisoft PhotoScan, Pix4Dmapper, and 3Dsurvey. As a result of photogrammetric image processing, 3D dense point clouds, mesh models, and digital surface models were generated. For validating the UAS-SfM results, the models were compared with the TLS model and field measurements; absolute accuracy assessments and model versus model comparisons were performed. The most accurate and high-quality 3D models comparable to TLS models were obtained from oblique images. PhotoScan generated relatively more accurate 3D models with the horizontal and vertical accuracy of root mean square error 0.025 and 0.02 m, respectively. The maximum discrepancies in dimensions and volume measurements from the UAS-SfM-derived point cloud were at the level of 0.01 m and 0.66 m3 (0.03%). The results highlighted that using a low-cost UAS and SfM photogrammetry, high-accuracy and high-quality 3D models can be generated. The results confirm that with respect to its rapid data acquisition and centimeter-level accuracy, UAS-SfM can be considered as a suitable alternative approach to conventional measurement techniques, especially in engineering and emergency response.

Small target location algorithm based on optimized template matching

At present, few published template matching algorithms can achieve the performance of commercial software. This paper studies and optimizes the existing geometric feature template matching algorithm, and combines it with the improved Fourier Merlin transform registration algorithm, and finally obtains a fast small target location algorithm. The matching time of the algorithm is 1-4ms, its positioning accuracy is sub-pixel level, the angle accuracy is less than 1 °, and it has good robustness to illumination. The experimental data show that the algorithm meets the requirements of real-time and accurate industrial automation production.

EVALUATION OF SDSM MODELS FOR CLIMATE PREDICTIONS IN BANGLADESH

As one of the poorest countries in the world, agriculture is Bangladesh’s economic pillar, leading to Bangladesh’s economy becoming vulnerable to global warming. The generation of high-resolution climate predictions in Bangladesh can help to reduce the huge damage and losses inflicted by disasters linked to climate. The statistical downscaling model (SDSM) is the most widely used software on robust climate downscaling and prediction analysis. In this study, by using the SDSM model, we established the statistical relationship between observed climate data in Bangladesh and the large-scale low-resolution NCEP data and used three statistical indicators to evaluate the prediction performance of the SDSM software. Our results show that the SDSM software is more suitable for forecasting humidity/temperature in Bangladesh than rainfall.

A METRICS -BASED MODEL FOR ESTIMATING THE MAINTENANCE EFFORT OF PYTHON SOFTWARE

Software project management includes a substantial area for estimating software maintenance effort. Estimation of software maintenance effort improves the overall performance and efficiency of software. The Constructive Cost Model (COCOMO) and other effort estimation models are mentioned in literature but are inappropriate for Python programming language. This research aimed to modify the Constructive Cost Model (COCOMO II) by considering a range of Python maintenance effort influencing factors to get estimations and incorporated size and complexity metrics to estimate maintenance effort. A within-subjects experimental design was adopted and an experiment questionnaire was administered to forty subjects aiming to rate the maintainability of twenty Python programs. Data collected from the experiment questionnaire was analyzed using descriptive statistics. Metric values were collected using a developed metric tool. The subject ratings on software maintainability were correlated with the developed model’s maintenance effort, a strong correlation of 0.610 was reported meaning that the model is valid.

Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study

PurposePersistent nonsolid nodules (NSNs) usually exhibit an indolent course and may remain stable for several years; however, some NSNs grow quickly and require surgical excision. Therefore, identifying quantitative features capable of early discrimination between growing and nongrowing NSNs is becoming a crucial aspect of radiological analysis. The main purpose of this study was to evaluate the performance of an open-source software (ImageJ) to predict the future growth of NSNs detected in a Caucasian (Italian) population.Material and methodsWe retrospectively selected 60 NSNs with an axial diameter of 6–30 mm scanned with the same acquisition-reconstruction parameters and the same computed tomography (CT) scanner. Software-based analysis was performed on thin-section CT images using ImageJ. For each NSNs, several quantitative features were extracted from the baseline CT images. The relationships of NSN growth with quantitative CT features and other categorical variables were analyzed using univariate and multivariable logistic regression analyses.ResultsIn multivariable analysis, only the skewness and linear mass density (LMD) were significantly associated with NSN growth, and the skewness was the strongest predictor of growth. In receiver operating characteristic curve analyses, the optimal cutoff values of skewness and LMD were 0.90 and 19.16 mg/mm, respectively. The two predictive models that included the skewness, with or without LMD, exhibited an excellent power for predicting NSN growth.ConclusionAccording to our results, NSNs with a skewness value > 0.90, specifically those with a LMD > 19.16 mg/mm, should require closer follow-up due to their higher growth potential, and higher risk of becoming an active cancer.

Analyze the Performance of Software by Machine Learning Methods for Fault Prediction Techniques

Trend of using the software in daily life is increasing day by day. Software system development is growing more difficult as these technologies are integrated into daily life. Therefore, creating highly effective software is a significant difficulty. The quality of any software system continues to be the most important element among all the required characteristics. Nearly one-third of the total cost of software development goes toward testing. Therefore, it is always advantageous to find a software bug early in the software development process because if it is not found early, it will drive up the cost of the software development. This type of issue is intended to be resolved via software fault prediction. There is always a need for a better and enhanced prediction model in order to forecast the fault before the real testing and so reduce the flaws in the time and expense of software projects. The various machine learning techniques for classifying software bugs are discussed in this paper.

The first steps for adapting an artificial intelligence emotion expression recognition software for emotional management in the educational context

AbstractTo test the suitability of an automatic system for emotional management in the classroom following the control‐value theory of achievement emotions (CVT) framework, the performance of an emotional expression recognition software of our creation is evaluated in an online synchronous context. Sixty students from the Faculty of Education at the University of Alicante participated in 16 educational activities recording close‐ups of their faces and completing the AEQ emotional self‐report, as well as detailed reports from the subsequent review of their videos. In addition, they completed the VCQ‐36 test to measure their volitional competencies and relate their influence on their emotional response. The results indicate a high coherence between the emotional expressions detected by the automatic system and the detailed emotional self‐reports, but insufficient precision to meet the CVT requirements. On the other hand, both the AEQ test results and the emotion expression recognition software suggest students' preference for participative activities as opposed to passive ones. Meanwhile, statistical analysis results indicate that volitional competencies seem to influence the emotional response of students in the educational context, although the AI system does not show sufficient sensitivity in this field. Implications and limitations of this study for future work are discussed. Practitioner notesWhat is already known about this topic Student motivation and involvement in the learning process are highly related to appropriate emotional regulation, which can be associated with particular educational activities, strategies and methodologies. Deep learning technology based on convolutional neural networks feeds automatic systems focused on facial expression recognition from image analysis. What this paper adds There is high coherence between the emotional expressions detected by the AI system and the students' emotional self‐reports, but the AI system provides just emotional valences, insufficient to meet the CVT framework. Both emotional self‐reports and the emotion recognition software suggest students' preference for active educational activities as opposed to passive ones. Volitional competencies seem to influence the emotional response of students in the educational context. Implications for practice and/or policy It is possible to use automatic systems to effectively monitor the emotional response of students in the learning process. Only if sensitivity improved, a real‐time, easy‐to‐interpret emotional expression recognition software interface could be implemented to assist teachers with the emotional management of their classes within the CVT framework, maximizing their motivation and engagement.

An Interactive Spectral Analysis Tool for Chemical Identification and Quantification of Gas-Phase Species in Complex Spectra.

A spectral analysis tool has been developed to interactively identify and quantify individual gas-phase species from complex infrared absorbance spectra obtained from laboratory or field data. The SpecQuant program has an intuitive graphical interface that accommodates both reference and experimental data with varying resolution and instrumental lineshape, as well as algorithms to readily align the wavenumber axis of a sample spectrum with the raster of a reference spectrum. Using a classical least squares model in conjunction with reference spectra such as those from the Pacific Northwest National Laboratory (PNNL) gas-phase infrared database or simulated spectra derived from the HITRAN line-by-line database, the mixing ratio of each identified species is determined along with its associated estimation error. After correcting the wavelength and intensity of the field data, SpecQuant displays the calculated mixing ratio versus the experimental data for each analyte along with the residual spectrum with any or all analyte fits subtracted for visual inspection of the fit and residuals. The software performance for multianalyte quantification was demonstrated using moderate resolution (0.5 cm-1) infrared spectra that were collected during the time-resolved infrared photolysis of methyl iodide.

REAL-TIME PLOTTING OF BIOMEDICAL SIGNALS IN APPLICATIONS UTILIZING WINDOWS PRESENTATION FOUNDATION TECHNOLOGY

Abstract: Utilization of personal computers in medicine requires strong emphasis on software performance. Contemporary programming languages give engineers great flexibility in developing applications. Since these technologies are more and more complex, there is a need to analyze methods of achieving desired results taking into account time requirements. This paper focuses mainly on development of plotting module using Windows Presentation Foundation library, which is one of the newest tool for software developers. Aspects of plotter creation for technologically limited screen are covered and conclusions are summarized giving recommendations regarding the topic. General methods of drawing signal on a raster surface are also discussed. Since medical signals in monitoring range consist of frequencies up to about 100 Hz, one has to take into account aspects of signal scaling in order to not to make its important parts invisible. The main problem is that personal computer screen, e.g. a monitor, is purely digital in nature; actually it is the set of pixels (consisting of three components). Because resolution is limited, the signal must be limited in frequency to the range which can be shown at a given plotting speed. This paper describes these aspects of signal plotting as well as the method for virtually increasing pixel resolution by using particular components of a pixel. This paper is a summary giving a general idea of the capabilities of the above mentioned WPF technology and the basis for creating such programs using methods such as: WriteableBitmap method, DrawingVisual method, Canvas Method type 1, Canvas Method type 2.

Enhanced network load balancing technique for efficient performance in software defined network.

Hash collisions and redirection of loads are major limitations for recent Hash IP algorithms. To overcome this, we propose a new Hash IP algorithm dubbed HDW in network load balancing (NLB) to increase network's efficiency, availability and scalability. We achieve the new Hash IP load balancing algorithm via a constructive merger with weighted scheduler (WS) technique and dynamic switching of routing path (DSP). This helps to reduce delays, jitters and additionally assures some level of security owing to the hashing process. Our findings after comprehensive simulations and performance evaluation depicts that our proposed HDW algorithm is relatively efficient as against other related load balancing algorithms for software defined network.

Algorithm Analysis of Face Recognition Robot Based on Deep Learning

With the continuous improvement of computer software and hardware performance, a large amount of image and video data can be easily collected and quickly transmitted, and new recognition methods that introduce deep learning are emerging, making the application and research of face recognition technology. The value is also increasingly prominent. The purpose of this paper is to study the face recognition robot implementation algorithm based on deep learning. The research background and significance of face recognition and expression recognition, which are the core of facial biological information extraction, are introduced. The face feature extraction network structure of Inception-ResNet-V1 has been improved, and high recognition features of faces can be obtained. At the same time, the training of the feature extraction model of the self-built training set and the adjustment of hyperparameters are completed. Finally, the effectiveness of the improved network in this paper is fully verified in the LFW test set and the actual robot environment. It is verified by experiments that the proposed optimization method can improve the performance of the network. It also verified the significant research significance of the current deep learning direction through practice.

Automated Detection of Software Performance Antipatterns in Java-Based Applications

The detection of performance issues in Java-based applications is not trivial since many factors concur to poor performance, and software engineers are not sufficiently supported for this task. The goal of this manuscript is the automated detection of performance problems in running systems to guarantee that no quality-based hinders prevent their successful usage. Starting from software performance antipatterns, i.e., bad practices (e.g., extensive interaction between software methods) expressing both the problem and the solution with the purpose of identifying shortcomings and promptly fixing them, we develop a framework that automatically detects seven software antipatterns capturing a variety of performance issues in Java-based applications. Our approach is applied to real-world case studies from different domains, and it captures four real-life performance issues of Hadoop and Cassandra that were not predicted by state-of-the-art approaches. As empirical evidence, we calculate the accuracy of the proposed detection rules, we show that code commits inducing and fixing real-life performance issues present interesting variations in the number of detected antipattern instances, and solving one of the detected antipatterns improves the system performance up to 50%.

Malicious Software Detection based on URL-API Intensity Feature Selection Using Deep Spectral Neural Classification for Improving Host Security

In recent years, the internet of services has been more responsive to access through the development of various application program interfaces (API). Accessing an HTTP uniform resource locator (URL) contains malicious software intended by the attacker to create security breaches through the use of APIs from various services on the internet. By default, the non-attentive URL downloads and installs malware in the background without the user’s knowledge. The host does not analyze the API-URL security certificate contract due to the feature access by the user. Therefore, the current Machine Learning (ML) techniques only check malware signatures and certificates rather than analyzing URL behaviour based on the impact of a URL accessed from the internet. To address this problem, we propose a novel intelligent malicious software based on URL-API intensity feature selection (IFS) and deep spectral neural classification (DSNC) for improving Host Security. Initially, the URL — successive certificate signing (SCS) of the user link accessibility is verified based on API download rate logs. This system identifies the best malware software. The Link Redirection Stability Rate (LRSR) is estimated based on the Redirection URL by accessing the direct link and redirect link. The domain transformation matrix (DTM) was created to create a pattern to access successive features. URL-API Intensity Feature Selection selects each estimated feature, and the selected features are based on soft-max logical activation with a recurrent neural network (RNN) optimized for deep learning. RNN is trained in the spectral domain for improving computation and efficiency. It predicts the class based on the risk of malicious weight to categorize class by reference. The proposed IFS-DSNC achieves accuracy of 95.6% than the other algorithms such as KNN, NB, CNN, LCS, GCRNC AGSCR. The experimental result shows that the proposed method provides better performance in finding malware software than the existing approaches, thereby improving the security against host breaching.

A Comprehensive Model of Android Software Aging and Rejuvenation Considering Battery Saving

The more complex the software system, the greater the number of possible combinations of defects that can cause errors, resulting in software defects that are difficult to isolate and expensive to correct in the development stage. An essential feature of such defects is a gradual deterioration in software performance finishing with software failure—software aging. Mobile devices are particularly vulnerable to software aging. Thus, there is a constant need for methods and tools to eliminate the effects of aging in mobile systems based on modeling the aging process, including the study of metrics and aging factors and the development of more reliable and adequate aging and rejuvenation models. This paper summarizes the previously developed Android software aging and rejuvenation models and presents a comprehensive model of aging and rejuvenation for the Android operating system. The comprehensive model is based on continuous-time Markov Chains and considers different aging levels, mobile device activity, and battery status. The aging and rejuvenation model can be used to assess the software quality, allows obtaining expressions for indicators of software rejuvenation efficiency, and can be used to design and select parameters of the software rejuvenation method considering battery saving.

Design of Proposed Software System for Prediction of Iliosacral Screw Placement for Iliosacral Joint Injuries Based on X-ray and CT Images.

One of the crucial tasks for the planning of surgery of the iliosacral joint is placing an iliosacral screw with the goal of fixing broken parts of the pelvis. Tracking of proper screw trajectory is usually done in the preoperative phase by the acquisition of X-ray images under different angles, which guide the surgeons to perform surgery. This approach is standardly complicated due to the investigation of 2D X-ray images not showing spatial perspective. Therefore, in this pilot study, we propose complex software tools which are aimed at making a simulation model of reconstructed CT (DDR) images with a virtual iliosacral screw to guide the surgery process. This pilot study presents the testing for two clinical cases to reveal the initial performance and usability of this software in clinical conditions. This model is consequently used for a multiregional registration with reference intraoperative X-ray images to select the slide from the 3D dataset which best fits with reference X-ray. The proposed software solution utilizes input CT slices of the pelvis area to create a segmentation model of individual bone components. Consequently, a model of an iliosacral screw is inserted into this model. In the next step, we propose the software CT2DDR which makes DDR projections with the iliosacral screw. In the last step, we propose a multimodal registration procedure, which performs registration of a selected number of slices with reference X-ray, and based on the Structural Similarity Index (SSIM) and index of correlation, the procedure finds the best match of DDR with X-ray images. In this pilot study, we also provide a comparative analysis of the computational costs of the multimodal registration upon various numbers of DDR slices to show the complex software performance. The proposed complex model has versatile usage for modeling and surgery planning of the pelvis area in fractures of iliosacral joints.