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Empirical research aims to establish generalizable claims from data. Such claims involve concepts that often must be measured indirectly by using indicators. Construct validity is concerned with whether one can justifiably make claims at the conceptual level that are supported by results at the operational level. We report a quantitative analysis of the awareness of construct validity in the software engineering literature between 2000 and 2019 and a qualitative review of 83 articles about human-centric experiments published in five high-quality journals between 2015 and 2019. Over the two decades, the appearance in the literature of the term construct validity increased sevenfold. Some of the reviewed articles we reviewed employed various ways to ensure that the indicators span the concept in an unbiased manner. We also found articles that reuse formerly validated constructs. However, the articles disagree about how to define construct validity. Several interpret construct validity excessively by including threats to internal, external, or statistical conclusion validity. A few articles also include fundamental challenges of a study, such as cheating and misunderstandings of experiment material. The diversity of topics discussed makes us recommend a minimalist approach to construct validity. We propose seven guidelines to establish a common ground for addressing construct validity in software engineering.

Theories can be represented as statistical models for empirical testing. There is a vast literature on model selection and multimodel inference that focuses on how to assess which statistical model, and therefore which theory, best fits the available data. For example, given some data, one can compare models on various information criterion or other fit statistics. However, what these indices fail to capture is the full range of counterfactuals. That is, some models may fit the given data better not because they represent a more correct theory, but simply because these models have more fit propensity - a tendency to fit a wider range of data, even nonsensical data, better. Current approaches fall short in considering the principle of parsimony (Occam’s Razor), often equating it with the number of model parameters. Here we offer a toolkit for researchers to better study and understand parsimony through the fit propensity of Structural Equation Models. We provide an R package (ockhamSEM) built on the popular lavaan package. To illustrate the importance of evaluating fit propensity, we use ockhamSEM to investigate the factor structure of the Rosenberg Self-Esteem Scale.

The data surveying this study mines methods for extracting informative and interesting knowledge from learning student datasets. The amount of data stored in education big databases is growing at an alarming rate. As such, data processing and knowledge discovery techniques can be used to discover interesting relationships between different characteristics and assessments of students. Initially to propose a novel self-analysis model which performs analysis about the different big data learning services. From the result provided by the user, the service will be ranked and analyze about the knowledge from daily activity gathered by the learner. The proposed model performs analysis to provide efficient Educational Data Mining (EDM) E-Learning system based on machine learning to ranking the users and performance evaluation in their activities. The existing method does not consider the learner activities, ranking and it focus on single feature based classification so the system doesn’t provide a better classification result. These issues to overcome introduce an Extended Multi-Labeled Gradient Boosting (XMGB) improve the classification performance both of imbalanced dataset. The method initial stage apply the Adaptive Expectation-Maximization Mixture (AEMM) method cluster the data various group and extract the E-learning multiple feature set. In this method to compute the classification time and memory provide an efficient result. Then introduce an academic activity based weight score analysis model to evaluate the learner daily activates and exam score its help to the ranking and cluster the data groups. In this proposed method analysis result to evaluate the following performance matrix: Recall, precision, F measure, classification accuracy. In this simulation analysis of the proposed method XMGB are provide an efficient classification with less time complexity compared to other existing methods.

Most existing studies on next point-of-interest (POI) recommendation assume that users deliver certain check-ins over individual POIs. In reality, we typically obtain uncertain check-ins due to the presence of collective POIs, which are gathering places of multiple individual POIs (e.g., shopping malls). On one hand, such uncertain check-ins over collective POIs hinder more accurate next POI recommendation for users due to the transition vanishing issue; on the other hand, the presence of collective POIs poses the challenge for businesses to select which collective POIs to locate in due to complicated competition and cooperation relations between businesses. As such, these collective POIs bring an unprecedented opportunity and necessity on recommendation for both users and businesses. Therefore, we propose novel solutions of location service beneficial for users-businesses. For users, we propose the STSP equipped with category- and location-aware encoders, to deliver more accurate next POI prediction by fusing rich context features. Regarding businesses, we explore their competition and cooperation relations from check-in records, based on which we derive the living environment (LE) of a business. Insight on site selection for businesses is provided by exploiting the LE, aiming to bring in more profits. Extensive empirical studies demonstrate the efficiency of our solutions.

Given the high uncertainty in R&D work, a key concern of R&D managers is to understand how to fail faster by managing time and cost efficiently among their R&D projects. This study investigates how using a variety of Six Sigma tools can differentially affect R&D project time and cost performance. Our data are collected from 35 R&D projects in an industrial R&D center, and a total of 30 different Six Sigma tools were found to be used in varying frequencies. We analyze this problem in two phases. First, by analyzing the frequencies of tool use in the 35 projects through factor analysis, we find the application of Six Sigma tools can be guided by two distinct patterns of tool use: design-oriented tools (DT) and non-design-oriented tools (non-DT). DT include quality function deployment and failure modes and effects analysis (FMEA) for defining and understanding of the focal problems, while non-DT include Pareto chart and design of experiment for generating and testing solutions for the defined problems. Second, based on the two categories of tool use, we further analyze the effects of the two patterns of tool use on project duration and project cost. Results show that using a variety of DT is associated with faster project completion and non-DT lower project cost. In addition, the use of Six Sigma tools (both DT and non-DT) is associated with shorter durations and higher costs for complex projects but lower costs and longer durations for less complex projects. That is, different modes of trade-ff between time and cost performance can occur when project complexity is considered as a moderating factor. Overall, our findings demonstrate new insights into how using a variety of problem-solving tools affects R&D project cost and duration simultaneously.

Previous literature addressed organizations’ adaptiveness to ever-changing business environments and investigated the concept of agility. However, extant agility research primarily covers the corporate and project levels and is typically located in the information systems and operations management fields. Relatively little quantitative research in innovation management literature exists, and those studies approached the concept solely from an outcome perspective (i.e., increased adaptiveness) instead of elucidating how organizations should organize themselves to be agile (i.e., a capability perspective). This article addresses these shortcomings and adopts a capability perspective since no empirical studies have examined agile R&D units’ organization (ARDO). Drawing on dynamic capabilities theory, we develop a measure of ARDO, conceptualizing it as a second-order construct, consisting of six dimensions: a culture of agile values, customer integration, autonomy, an iterative work method, cross-functional capabilities, and flat hierarchies. We validated the measurement by conducting three studies to ensure content, structural, and nomological validity. We applied structural equation modeling on a sample of 175 R&D managers and cross validated our findings on different hierarchical levels via a sample of 454 R&D employees. The results confirm the second-order nature of the ARDO measure and provide evidence of its positive relationship with front-end success. We advance scarce quantitative research on agility's neglected capability perspective and contribute to the innovation management field by facilitating further empirical research on agile R&D units’ antecedents and outcomes in the context of physical product development.

Mobile apps provide new opportunities to people with disabilities to act independently in the world. Motivated by this trend, researchers have conducted empirical studies by using the inaccessibility issue rate of each page (i.e., screen level) to represent the characteristics of mobile app accessibility. However, there still lacks an empirical investigation directly focusing on the issues themselves (i.e., issue level) to unveil more fine-grained findings, due to the lack of an effective issue detection method and a relatively comprehensive dataset of issues. To fill in this literature gap, we first propose an automated app page exploration tool, named Xbot, to facilitate app accessibility testing and automatically collect accessibility issues by leveraging the instrumentation technique and static program analysis. Owing to the relatively high activity coverage (around 80%) achieved by Xbot when exploring apps, Xbot achieves better performance on accessibility issue collection than existing testing tools such as Google Monkey. With Xbot, we are able to collect a relatively comprehensive accessibility issue dataset and finally collect 86,767 issues from 2,270 unique apps including both closed-source and open-source apps, based on which we further carry out an empirical study from the perspective of accessibility issues themselves to investigate novel characteristics of accessibility issues. Specifically, we extensively investigate these issues by checking 1) the overall severity of issues with multiple criteria, 2) the in-depth relation between issue types and app categories, GUI component types, 3) the frequent issue patterns quantitatively, and 4) the fixing status of accessibility issues.

The aim of this study is to figure out advantages and applications of infrared rays system in hospitals, at home and in industry. Methodology that authors use is empirical research and practices and experiences in infrared rays system applications in emerging markets such as Vietnam. Research Results find out that danger for infrared products users as it goes with high temperature, however there are many advantages such as kitchen stoves, temperature guns. Last but not least, infrared rays products has other applications in hospitals. Finally, we suggest there are more further researches on applications of infrared rays products in future. Risk management also need for infrared rays products applications.

Vision systems applied to drones, automatic vehicles, and robots have become an increasingly popular sensing method. However, vision sensors that make up these systems are vulnerable to malicious input attacks, which can lead to serious consequences. Prior work on attacking cameras of automatic vehicles shows that lasers can cause failure of camera-based functionalities, but it lacks analysis of the results and does not conduct experiments in the actual scenarios.In this paper, a laser-based attack on cameras and binocular vision sensors of drones is presented. First, we propose a threat model that describes how an adversary attacks the drone then perform feasibility analysis of the attack from theory and practice. Next, we design multi-variable experiments in the lab to systematically study the effectiveness of the attack, and further analyze how each variable affects the results. To get intuitive and fine-grained results, multidimensional image similarity is used to measure the effects. In particular, experiments in the actual scenarios are carried out, and results show that the attack can make obstacle avoidance, target recognition and tracking completely failed. Finally, lightweight countermeasures based on hardware and software are proposed to improve sensor resilience against the attack.

The application of model transformations is a critical component in Model-Driven Engineering (MDE). To ensure the correctness of the generated models, these model transformations need to be extensively tested. However, during the regression testing of these model transformations, it becomes too costly to frequently run a large number of test cases. Test case prioritization techniques are needed to rank the test cases and help the tester during the regression testing to be more efficient. The objective is to rank the fault revealing test cases higher so that a tester can only execute the top ranked test cases and still be able to detect as many faults as possible in the case of limited budget and resources. The aim of this paper is to present a test prioritization approach for the regression testing of model transformations. The approach is based on exploiting the rule coverage information of the test cases. The paper presents an empirical study which compares several techniques introduced by our approach for prioritizing test cases. The approach is complemented with a tool that implements the proposed techniques and can automatically generate test case orderings.

Real-world infrastructures modeled as complex networks have been popular topics in recent decades. As these infrastructures develop, becoming critical to support millions of people, their resilience in the face of disasters and attacks becomes an increasing concern to the engineers and stakeholders. Using a graph theory and complex network approach, we conducted an empirical study on the resilience of five metro, or subway systems selected globally. A multi-phase resilience framework was implemented from the resilience cycle concept, including preparedness, robustness, recovery, and adaptation. It also incorporated the historical flow data into the resilience analysis to account for user behavior impacts. The paper demonstrates a systematic comparison of five metro networks and explores trends in each resilience phase. Generally, diverse performances were observed among the five metro networks, with the Singapore metro showing the best resilience. It is robust to both random disruption and targeted attacks. Analysis suggests its advantage may come from both its moderate heterogeneous topology and the flow pattern, exhibiting the smallest average travel distance. Besides, the result highlights the significance of commuters' relocation behavior between neighboring stations in the recovery stage. For the adaptation from a long-term perspective, we developed a screening tool to investigate the impact of the network expansion on the existing part of network by mapping its relationship with the characteristics of the newly connected nodes. In general, the observation of the networks' different behaviors in flow-weighted analysis symbolizes that flow is necessary for consideration in the resilience design of real-world transportation systems.

Following the highly pervasive and effective use of agile methods at the team level, many software organisations now wish to replicate this success at the organisational level, adopting large-scale agile methods such as SAFe, Scrum-at-Scale, and others. However, this has proven significantly challenging. An analysis of the extant literature reveals a disparate set of studies across each individual method, with no cross-method comparison based on empirical evidence. This systematic literature review compares the main large-scale agile methods, namely SAFe, LeSS, Scrum-at-Scale, DAD, and the Spotify model. It is the first study to analyse and compare each of the method's principles, practices, tools, and metrics in a standardised manner. For each method, it presents not just the original method specifications but also all extensions and modifications to each method proposed by subsequent empirical research. It includes in this comparison not just commercial large-scale methods but also those that have been custom-built in organisations such as Nokia, Ericsson, and others. Based on the findings reported in this study, practitioners can make a more informed decision as to which commercial method or method component or, indeed, custom-built method is better suited to their needs. Our study reveals a number of theoretical and practical issues in the current literature, such as an emphasis on the practices of commercial frameworks at the expense of their underlying principles, or indeed any of the custom method. A set of challenges and success factors associated with the use of large-scale agile methods are identified. The study also identifies a number of research gaps to be addressed across methods.

Card fraud is an important issue and incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant fraudulent behavior patterns in the online detection system. Therefore, in this work, we propose a spatial-temporal attention-based graph network (STAGN) for credit card fraud detection. In particular, we learn the temporal and location-based transaction graph features by a graph neural network first. Afterwards, we employ the spatial-temporal attention on top of learned tensor representations, which are then fed into a 3D convolution network. The attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. After that, we conduct extensive experiments on the real-word card transaction dataset. The result shows that STAGN performs better than other state-of-the-art baselines in both AUC and precision-recall curves. Moreover, we conduct empirical studies with domain experts on the proposed method for fraud detection and knowledge discovery; the result demonstrates its superiority in detecting suspicious transactions, mining spatial and temporal fraud hotspots, and uncover fraud patterns. The effectiveness of the proposed method in other user behavior-based tasks is also demonstrated. Finally, in order to tackle the challenges of big data, we integrate our proposed STAGN into the fraud detection system as the predictive model and present the implementation detail of each module in the system.

We report on a large-scale empirical study investigating the relevance of socio-technical congruence over key basic software quality metrics, namely, bugs and churn. In particular, we explore whether alignment or misalignment of social communication structures and technical dependencies in large software projects influences software quality. To this end, we have defined a quantitative and operational notion of socio-technical congruence, which we call socio-technical motif congruence (STMC). STMC is a measure of the degree to which developers working on the same file or on two related files, need to communicate. As socio-technical congruence is a complex and multi-faceted phenomenon, the interpretability of the results is one of our main concerns, so we have employed a careful mixed-methods statistical analysis. In particular, we provide analyses with similar techniques as employed by seminal work in the field to ensure comparability of our results with the existing body of work. The major result of our study, based on an analysis of 25 large open-source projects, is that STMC is not related to project quality measures -- software bugs and churn -- in any temporal scenario. That is, we find no statistical relationship between the alignment of developer tasks and developer communications on the one hand, and project outcomes on the other hand. We conclude that, wherefore congruence does matter as literature shows, then its measurable effect lies elsewhere., 29 pages, 21 figures. To appear in IEEE Transactions on Software Engineering

In recent years, Python has experienced explosive growth in adoption, particularly among open source projects. While Python's dynamically-typed nature provides developers with powerful programming abstractions, that same dynamic type system allows for type-related defects to accumulate in code bases. To aid in the early detection of type-related defects, type annotations were introduced into the Python ecosystem (i.e., PEP-484) and static type checkers like mypy have appeared on the market. While applying a type checker like mypy can in theory help to catch type-related defects before they impact users, little is known about the real impact of adopting a type checker to reveal defects in Python projects. In this paper, we study the extent to which Python projects benefit from such type checking features. For this purpose, we mine the issue tracking and version control repositories of 210 Python projects on GitHub. Inspired by the work of Gao et al. on type-related defects in JavaScript, we add type annotations to test whether detects an error that would have helped developers to avoid real defects. We observe that 15% of the defects could have been prevented by mypy. Moreover, we find that there is no significant difference between the experience level of developers committing type-related defects and the experience of developers committing defects that are not type-related. In addition, a manual analysis of the anti-patterns that most commonly lead to type-checking faults reveals that the redefinition of Python references, dynamic attribute initialization and incorrectly handled Null objects are the most common causes of type-related faults. Since our study is conducted on fixed public defects that have gone through code reviews and multiple test cycles, these results represent a lower bound on the benefits of adopting a type checker. Therefore, we recommend incorporating a static type checker like mypy into the development workflow, as not only will it prevent type-related defects but also mitigate certain anti-patterns during development.

As a software system evolves, its architecture tends to decay, leading to the occurrence of architectural elements that become resistant to maintenance or prone to defects. To address this problem, engineers can significantly benefit from determining which architectural elements will decay before that decay actually occurs. Forecasting decay allows engineers to take steps to prevent decay, such as focusing maintenance resources on the architectural elements most likely to decay. To that end, we construct novel models that predict the quality of an architectural element by utilizing multiple architectural views (both structural and semantic) and architectural metrics as features for prediction. We conduct an empirical study using our prediction models on 38 versions of five systems. Our findings show that we can predict low architectural quality, i.e., architectural decay, with high performanceeven for cases of decay that suddenly occur in an architectural module. We further report the factors that best predict architectural quality.

Stack Overflow hosts millions of solutions that aim to solve developers' programming issues. Stack Overflow becomes a code hosting website where developers actively share its code. However, code snippets on Stack Overflow may contain security vulnerabilities, and if shared carelessly, such snippets can introduce security problems in software systems. In this paper, we empirically study the prevalence of the Common Weakness Enumeration -- CWE, in code snippets of C/C++ related answers. We explore the characteristics of Codew, i.e., code snippets that have CWE instances, in terms of the types of weaknesses, the evolution of Codew, and who contributed such code snippets. We find that: 1) 36% (i.e., 32 out of 89) CWE types occurred in Codew on Stack Overflow. Particularly, CWE-119, i.e., improper restriction of operations within the bounds of a memory buffer, is common in both answer code snippets and real-world software systems. Furthermore, the proportion of Codew, doubled from 2008 to 2018 after normalizing by the total number of C/C++ snippets in each year. 2) In general, code revisions are associated with a reduction in the number of code weaknesses. However, the majority of Codew had weaknesses introduced in the first version of the code, and these Codew were never revised since then. Only 7.5% of users who contributed C/C++ code snippets posted or edited code with weaknesses. Users contributed fewer code with CWE weakness when they were more active -- either revised more code snippets or had a higher reputation. We also find that some users tended to have the same CWE type repeatedly in their various code snippets. Our empirical study provides insights to users who share code snippets on Stack Overflow so that they are aware of the potential security issues. To understand the community feedback about improving code weaknesses by answer revisions, we also conduct a pilot user study and 62.5% of our suggested revisions are adopted by the community. Stack Overflow can perform CWE scanning for all the code that is hosted on its platform. Further research is needed to improve the quality of the crowdsourced knowledge on Stack Overflow.

In the era of Connected and Autonomous Vehicles, platooning has the potential to increase roadway capacity and reduce energy consumption. However, as vehicles try to form platoons they may also expend extra energy. Also, depending on its position within a platoon, the energy savings of each vehicle can be different. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this paper, we develop a simulation-optimization framework to tackle this challenge of quantifying energy savings from platooning. Our optimization model determines vehicle-to-platoon assignments given the current location, speed, and destination of all the vehicles and platoons on the freeway. The simulation model takes these platooning decisions from the optimization model and implements them. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. To quantify the energy savings, the system is simulated with and without platooning. That is, the optimization model is turned off when assessing the performance of the system without platooning. In addition to the simulation-optimization framework, an accurate energy consumption model is developed in this paper, which is inspired by the work of Tadakuma and colleagues. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multi-vehicle formations unifying both physical mechanisms and existing empirical study data. Our results show that a system wide savings of about 3% can be realized over 160 kilometers when platoons are formed strategically.

The release note is one of the most important software artifacts that serves as a bridge for communication among stakeholders. Release notes contain a set of crucial information, such as descriptions of enhancements, improvements, potential issues, development, evolution, testing, and maintenance of projects throughout the whole development lifestyle. A comprehensive understanding of what makes a good release note and how to write one for different stakeholders would be highly beneficial. However, in practice, the release note is often neglected by stakeholders and has not to date been systematically investigated by researchers. In this paper, we conduct a mixed methods study to investigate the use of release notes in practice. We first conducted a large-scale empirical study of 32,425 release notes from 1,000 GitHub projects to understand current contents and information found in real-world release notes. We then performed interviews with 15 practitioners and an online survey with 314 respondents to investigate how key stakeholders perceive release notes. From the analysis of these data, we summarized eight categories of information that are normally documented in release notes in GitHub projects. We found that stakeholders consider that well-formed release notes have a positive impact on software development, such as software evolution. We concluded 28 statements grouped into eight topics based on stakeholders' opinions. There exist significant discrepancies between different stakeholders on how release notes should be written and used. Our study provides new insights on release notes and facilitates stakeholders to better take advantage of them during software development.

Recently, Social Media (SM)’s adoption as educational instruments in Higher Education (HE) has been gaining popularity among researchers and practitioners. Nonetheless it has been adopted to support various learning approaches such as Cooperative Learning (CL). Despite the growing research in this field, few studies have proposed a comprehensive SM integration framework as well as empirical studies to evaluate its effectiveness. This paper examines how SM is being used as a pedagogical tool in HE and identifies major factors that drives SM’s inclusion in HE classrooms. In addition, the paper investigates the effectiveness of a previously proposed integration framework which was developed to promote CL using a popular SM platform, LinkedIn. A survey was administered to collect data from students in different subjects at a university in Australia. Structural Equation Modelling (SEM) was adopted for data analysis using IBM’s SPSS26 and AMOS26. One of the notable results of this study is the significant positive relation found between the use of the adopted integration framework and CL, SM challenges’ mitigation, students’ intention to use SM and their learning process. The findings of this study have important implications for practice and pave the way for future empirical studies.

How to extract useful knowledge from a high-dimensional and sparse (HiDS) matrix efficiently is critical for many big data-related applications. A latent factor (LF) model has been widely adopted to address this problem. It commonly relies on an iterative learning algorithm like stochastic gradient descent. However, an algorithm of this kind commonly consumes many iterations to converge, resulting in considerable time cost on large-scale datasets. How to accelerate an LF model's training process without accuracy loss becomes a vital issue. To address it, this study innovatively proposes a multilayered-and-randomized latent factor (MLF) model. Its main idea is two-fold: a) adopting randomized-learning to train LFs for implementing a ‘one-iteration’ training process for saving time; and 2) adopting the principle of a generally multilayered structure as in a deep forest or multilayered extreme learning machine to structure its LFs, thereby enhancing its representative learning ability. Empirical studies on six HiDS matrices from real applications demonstrate that compared with state-of-the-art LF models, an MLF model achieves significantly higher computational efficiency with satisfactory prediction accuracy. It has the potential to handle LF analysis on a large scale HiDS matrix with real-time requirements.

With the popularity of graph applications, frequent pattern mining (FPM) has been playing a significant role in many domains, such as social networks and bioinformatics. However, due to the exponential time complexity of FPM, it is a challenge for most existing techniques in big dense graphs, such as social graphs. In this paper, with the defined concept of social pattern, a corresponding linear time computable support calculation measurement, called Minimum Independent Individual based support, is proposed. Then, we adopt the concept of pathgraph to store the appearance of social patterns and propose a novel approach (SocMi) to solve the problem of frequent social pattern mining. Additionally, in order to reduce the exponential time consumption to explore big graphs, an approximate approach (ASocMi) with a quick exploration strategy is proposed. Moreover, the proposed approaches have been further optimized by using cache during processing. Finally, an extensive empirical study in real-world social graphs has demonstrated the effectiveness and efficiency of the proposed approaches compared with the state-of-the-art approaches.

Automated vehicles (AVs) are one critical application area of artificial intelligence (AI). However, a lack of appropriate trust can be a major barrier to successfully introducing AVs into the market. The objective of this study is to summarize and synthesize the existing literature to gain a greater understanding of factors that will potentially influence the development of pedestrians’ trust in AVs over time. Since AVs will become part of a larger infrastructure system that influences more than just AV users, they should also be accepted by pedestrians and other road users. There is a need for pedestrians to form appropriate levels of trust toward AVs to achieve safe interaction with such vehicles in circumstances characterized by uncertainty and vulnerability. Consequently, factors relevant to the building of this appropriate trust must be understood. By integrating the reviewed empirical studies and related theories, a theoretical model has been proposed and developed, comprising three layers of variability in pedestrian-AV trust (dispositional trust, situational trust, and learned trust). Given that this is an emerging field of research, much still remains unknown, and this review identifies several gaps in current knowledge for each layer of trust, as well as providing suggestions for consideration in future studies. Additionally, the proposed model of pedestrian-AV trust can be useful to transportation researchers, practitioners, designers, and AV manufacturers for designing AVs and related transportation systems for the purposes of successfully integrating AVs into society, and calibrating pedestrians’ trust to the appropriate level.

The great success of numerous community-based open source software (OSS) is based on volunteers continuously submitting contributions, but ensuring sustainability is a persistent challenge in OSS communities. Although the motivations behind and barriers to OSS contributors' joining and retention have been extensively studied, the impacts of, reasons for and solutions to contribution abandonment at the individual level have not been well studied, especially for pull-based development. To bridge this gap, we present an empirical study on pull request abandonment based on a sizable dataset. We manually examine 321 abandoned pull requests on GitHub and then quantify the manual observations by surveying 710 OSS developers. We find that while the lack of integrators' responsiveness, the lack of contributors' time and interest remain the main reasons that deter contributors from participation, limitations during the processes of patch updating and consensus reaching can also cause abandonment. We also show the significant impacts of pull request abandonment on project management and maintenance. Moreover, we elucidate the strategies used by project integrators to cope with abandoned pull requests and highlight the need for a practical handover mechanism. We discuss the actionable suggestions and implications for OSS practitioners and tool builders, which can help to upgrade the infrastructure and optimize the mechanisms of OSS communities.

Traffic authorities implement management measures to route drivers towards socially-desired paths. The main goal of such measures is to steer the network from the inefficient but fair user equilibrium to the system optimum: the traffic state that minimizes total travel time (Wardrop, 1952). Here, the behavioral response to route advice is often not accounted for, since some drivers are advised to take a significant detour for the system’s benefit. Hence, these drivers may not comply with such advice, and the optimal state will not be achieved. However, empirical evidence shows that (some) users are receptive to advice that proposes reasonable routes that benefit the system (van Essen et al., 2020). In this study, we propose a social routing service that steers the traffic network towards an efficient but also fair, and, therefore, attainable and over time maintainable traffic state. This social routing system anticipates user responses: only a portion of the demand is assumed to receive and follow the advice, and the realized travel time difference among travelers is explicitly limited. Although empirical research shows that social routing has great potential in real life, there are theoretical challenges to be addressed before implementation. It is not yet known which paths to propose to which type of travelers, and how to efficiently calculate these paths. In this paper, we show that the theoretical challenges can be addressed by formulating and analyzing a bilevel optimization problem. This program calculates the best possible paths to be proposed to receptive travelers (those that use and comply with advice) and limits travel time differences in the resulting state. A critical issue in solving the bilevel problem is that the lower-level optimal solution is non-unique. We use techniques from variational analysis to show that the directional derivative of the lower-level link flow nonetheless exists. This generalized derivative can be efficiently found as a solution of a quadratic optimization problem but requires a suitable route flow solution as parameter. We provide an auxiliary linear program that finds this path flow solution efficiently. We use the directional derivative in a descent algorithm to solve the bilevel program. Numerical experiments show that a real-world implementation of the social routing service has the potential to substantially improve efficiency and preserve fairness at the same time. If only 25% of all travelers take a very small detour (less than 0.6 minutes, where a system optimum requires 3.2 minutes), 27% of the maximum-possible savings are achieved.

A wide body of empirical research has revealed the descriptive shortcomings of expected value and expected utility models of risky decision making. In response, numerous models have been advanced to predict and explain people’s choices between gambles. Although some of these models have had a great impact in the behavioral, social, and management sciences, there is little consensus about which model offers the best account of choice behavior. In this paper, we conduct a large-scale comparison of 58 prominent models of risky choice, using 19 existing behavioral data sets involving more than 800 participants. This allows us to comprehensively evaluate models in terms of individual-level predictive performance across a range of different choice settings. We also identify the psychological mechanisms that lead to superior predictive performance and the properties of choice stimuli that favor certain types of models over others. Moreover, drawing on research on the wisdom of crowds, we argue that each of the existing models can be seen as an expert that provides unique forecasts in choice predictions. Consistent with this claim, we find that crowds of risky choice models perform better than individual models and thus provide a performance bound for assessing the historical accumulation of knowledge in our field. Our results suggest that each model captures unique aspects of the decision process and that existing risky choice models offer complementary rather than competing accounts of behavior. We discuss the implications of our results on theories of risky decision making and the quantitative modeling of choice behavior. This paper was accepted by Yuval Rottenstreich, behavioral economics and decision analysis.

The ongoing COVID-19 pandemic highlights the importance of dashboards for providing critical real-time information. In order to enable people to obtain information in time and to understand complex statistical data, many developers have designed and implemented public-oriented COVID-19 "information dashboards" during the pandemic. However, development often takes a long time and developers are not clear about many people's information needs, resulting in gaps between information needs and supplies. According to our empirical study and observations with popular developed COVID-19 dashboards, this seriously impedes information acquirement. Our study compares people's needs on Twitter with existing information suppliers. We determine that despite the COVID-19 information that is currently on existing dashboards, people are also interested in the relationship between COVID-19 and other viruses, the origin of COVID-19, vaccine development, fake new about COVID-19, impact on women, impact on school/university, and impact on business. Most of these have not yet been well addressed. We also summarise the visualization and interaction patterns commonly applied in dashboards, finding key patterns between data and visualization as well as visualization and interaction. Our findings can help developers to better optimize their dashboard to meet people's needs and make improvements to future crisis management dashboard development.