Test case prioritization is one of the most practically useful activities in testing, specially for large scale systems. The goal is ranking the existing test cases in a way that they detect faults as soon as possible, so that any partial execution of the test suite detects maximum number of defects for the given budget. Test prioritization becomes even more important when the test execution is time consuming, e.g., manual system tests vs. automated unit tests. Most existing test case prioritization techniques are based on code coverage, which requires access to source code. However, manual testing is mainly done in a blackbox manner (manual testers do not have access to the source code). Therefore, in this paper, we first examine the existing test case prioritization techniques and modify them to be applicable on manual black-box system testing. We specifically study a coverage-based, a diversity-based, and a risk driven approach for test case prioritization. Our empirical study on four older releases of Mozilla Firefox shows that none of the techniques are strongly dominating the others in all releases. However, when we study nine more recent releases of Firefox, where the development has been moved from a traditional to a more agile and rapid release environment, we see a very signifiant difference (on average 65% effectiveness improvement) between the risk-driven approach and its alternatives. Our conclusion, based on one case study of 13 releases of an industrial system, is that test suites in rapid release environments, potentially, can be very effectively prioritized for execution, based on their historical riskiness; whereas the same conclusions do not hold in the traditional software development environments.We then study the same techniques on nine more recent 978-1-4799-7125-1/15/$31.00 ©2015 IEEE
Summary Test case prioritization is an important testing activity, in practice, specially for large scale systems. The goal is to rank the existing test cases in a way that they detect faults as soon as possible, so that any partial execution of the test suite detects the maximum number of defects for the given budget. Test prioritization becomes even more important when the test execution is time consuming, for example, manual system tests versus automated unit tests. Most existing test case prioritization techniques are based on code coverage, which requires access to source code. However, manual testing is mainly performed in a black‐box manner (manual testers do not have access to the source code). Therefore, in this paper, the existing test case prioritization techniques (e.g. diversity‐based and history‐based techniques) are examined and modified to be applicable on manual black‐box system testing. An empirical study on four older releases of desktop Firefox showed that none of the techniques were strongly dominating the others in all releases. However, when nine more recent releases of desktop Firefox, where the development has been moved from a traditional to a more agile and rapid release environment, were studied, a very significant difference between the history‐based approach and its alternatives was observed. The higher effectiveness of the history‐based approach compared with alternatives also held on 28 additional rapid releases of other Firefox projects – mobile Firefox and tablet Firefox. The conclusion of the paper is that test cases in rapid release environments can be very effectively prioritized for execution, based on their historical failure knowledge. In particular, it is the recency of historical knowledge that explains its effectiveness in rapid release environments rather than other changes in the process. Copyright © 2016 John Wiley & Sons, Ltd.
We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to their high owning and operating costs, long charging time, and the uneven spatial distribution of charging facilities. Moreover, the highly dynamic environment factors such as unpredictable traffic congestion, different passenger demands, and even the changing weather can significantly affect electric bus charging efficiency and potentially hinder the further promotion of large-scale electric bus fleets. To address these issues, in this article, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines, and 5,562 bus stops. Then, we investigate the electric bus network to understand its operating and charging patterns, and further verify the necessity and feasibility of a real-time charging scheduling. With such understanding, we design busCharging , a pricing-aware real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of busCharging , we implement it with the real-world data from Shenzhen, which includes GPS data of electric buses, the metadata of all bus lines and bus stops, combined with data of 376 charging stations for electric buses. The evaluation results show that busCharging dramatically reduces the charging cost by 23.7% and 12.8% of electricity usage simultaneously. Finally, we design a scheduling-based charging station expansion strategy to verify our busCharging is also effective during the charging station expansion process.
Our society is witnessing a rapid taxi electrification process. Compared to conventional gas taxis, a key drawback of electric taxis is their prolonged charging time, which potentially reduces drivers' daily operation time and income. In addition, insufficient charging stations, intensive charging peaks, and heuristic-based charging station choice of drivers also significantly decrease the charging efficiency of electric taxi charging networks. To improve the charging efficiency (e.g., reduce queuing time in stations) of electric taxi charging networks, in this paper, we design a fairness-aware Pareto efficient charging recommendation system called FairCharge, which aims to minimize the total charging idle time (traveling time + queuing time) in a fleet-oriented fashion combined with fairness constraints. Different from existing works, FairCharge considers fairness as a constraint to potentially achieve long-term social benefits. In addition, our FairCharge considers not only current charging requests, but also possible charging requests of other nearby electric taxis in a near-future duration. More importantly, we simulate and evaluate FairCharge with real-world streaming data from the Chinese city Shenzhen, including GPS data and transaction data from more than 16,400 electric taxis, coupled with the data of 117 charging stations, which constitute, to our knowledge, the largest electric taxi network in the world. The extensive experimental results show that our fairness-aware FairCharge effectively reduces queuing time and idle time of the Shenzhen electric taxi fleet by 80.2% and 67.7%, simultaneously.
Urban traffic sensing has been investigated extensively by different real-time sensing approaches due to important applications such as navigation and emergency services. Basically, the existing traffic sensing approaches can be classified into two categories by sensing natures, i.e., explicit and implicit sensing. In this article, we design a measurement framework called EXIMIUS for a large-scale data-driven study to investigate the strengths and weaknesses of two sensing approaches by using two particular systems for traffic sensing as concrete examples. In our investigation, we utilize TB-level data from two systems: (i) GPS data from five thousand vehicles, (ii) signaling data from three million cellphone users, from the Chinese city Hefei. Our study adopts a widely used concept called crowdedness level to rigorously explore the impacts of contexts on traffic conditions including population density, region functions, road categories, rush hours, holidays, weather, and so on, based on various context data. We quantify the strengths and weaknesses of these two sensing approaches in different scenarios and then we explore the possibility of unifying two sensing approaches for better performance by using a truth discovery-based data fusion scheme. Our results provide a few valuable insights for urban sensing based on explicit and implicit data from transportation and telecommunication domains.
Exploring cellphone network data has been proved to be a very effective way to understand urban populations because of the high penetration rate of cellphones. However, the state-of-the-art population models driven by cellphone data are typically built upon single cellphone networks, assuming the users in a particular cellphone network used are representative of all residents in the studied city with multiple cellphone networks. This assumption usually does not hold in the real world due to strategic spatial coverages and business concentrations of cellphone companies, which lead to data biases, and thus overfitting of resultant population models. To address this issue, we design a model called MultiCell to model real-time urban populations from multiple cellphone networks with two novel techniques: (i) a network realignment technique to integrate individual cell-tower spatial distributions from multiple cellphone networks for finer granular population modeling; (ii) a data fusion technique based on cross-network training to design a population model based on multiple network data. We implement MultiCell in the Chinese city Shenzhen based on three cellphone networks with 10 million active users and their daily data records at 11 thousand cell towers. We evaluate MultiCell by comparing it to the state-of-the-art models driven by single cellphone networks, and the evaluation results show that MultiCell outperforms them by 27% in terms of accuracy. Finally, we cross-validate MultiCell with three transportation systems with more than 8 million passengers to investigate its performances.
Accurate and up-to-date digital road maps are the foundation of many mobile applications, such as navigation and autonomous driving. A manually-created map suffers from the high cost for creation and maintenance due to constant road network updating. Recently, the ubiquity of GPS devices in vehicular systems has led to an unprecedented amount of vehicle sensing data for map inference. Unfortunately, accurate map inference based on vehicle GPS is challenging for two reasons. First, it is challenging to infer complete road structures due to the sensing deviation, sparse coverage, and low sampling rate of GPS of a fleet of vehicles with similar mobility patterns, e.g., taxis. Second, a road map requires various road properties such as road categories, which is challenging to be inferred by just GPS locations of vehicles. In this paper, we design a map inference system called coMap by considering multiple fleets of vehicles with Complementary Mobility Features. coMap has two key components: a graph-based map sketching component, a learning-based map painting component. We implement coMap with the data from four type-aware vehicular sensing systems in one city, which consists of 18 thousand taxis, 10 thousand private vehicles, 6 thousand trucks, and 14 thousand buses. We conduct a comprehensive evaluation of coMap with two state-of-the-art baselines along with ground truth based on OpenStreetMap and a commercial map provider, i.e., Baidu Maps. The results show that (i) for the map sketching, our work improves the performance by 15.9%; (ii) for the map painting, our work achieves 74.58% of average accuracy on road category classification.
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