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Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning-based KT models have shown significant predictive performance compared with traditional models. However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are several ways to achieve high accuracy in student performance prediction but diagnostic and prognostic reasoning is more critical in learning sciences. Since KT problem has few observable features (problem ID and student's correctness at each practice), we extract meaningful latent features from students' response data by using machine learning and data mining techniques. In this work, we present Interpretable Knowledge Tracing (IKT), a simple model that relies on three meaningful latent features: individual skill mastery, ability profile (learning transfer across skills), and problem difficulty. IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning-based student models. IKT also shows better student performance prediction than deep learning-based student models without requiring a huge amount of parameters. We conduct ablation studies on each feature to examine their contribution to student performance prediction. Thus, IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems., AAAI Symposium on Educational Advances in Artificial Intelligence EAAI-22. arXiv admin note: text overlap with arXiv:2012.12218

International audience; Mobile devices offer a communication channel in the financial market. They support mobile trader informedness, the degree to which traders have the information to make decisions about buying and selling stocks. Using market and social media data for three months in 2012 in Korea, we examine how trader informedness from social sentiment affects their buying and selling behavior compared to traders who use the traditional channel without mobile phones. Our design involves econometrics, text analysis, machine learning, and explanatory empiricism to identify causal inferences about how mobile phones affect trader behavior. We show the impacts of social sentiment in the mobile channel have different trading patterns than the traditional channel. Mobile traders make more buys with negative sentiment and sell more with positive sentiment. Traders perceive greater informedness from social sentiment over time, though they may not be more informed. The timing and patterns of their buys and sells are different when daily sentiment is consolidated into a trend over time. They may act on signals leading to patterns of individualistic or contrarian differentiating behavior, opposite to herding behavior. As such, trader informedness plays a critical role due to the diffusion of social sentiment-related financial technology in the past decade.

International audience; Due to the rise of AI applications, machine learning (ML) libraries, often written in Python, have become far more accessible. ML libraries tend to be updated periodically, which may deprecate existing APIs, making it necessary for application developers to update their usages. In this paper, we build a tool to automate deprecated API usage updates. We first present an empirical study to better understand how updates of deprecated ML API usages in Python can be done. The study involves a dataset of 112 deprecated APIs from Scikit-Learn, TensorFlow, and PyTorch. Guided by the findings of our empirical study, we propose MLCatchUp, a tool to automate the updates of Python deprecated API usages, that automatically infers the API migration transformation through comparison of the deprecated and updated API signatures. These transformations are expressed in a Domain Specific Language (DSL). We evaluate MLCatchUp using a dataset containing 267 files with 551 API usages that we collected from public GitHub repositories. In our dataset, MLCatchUp can detect deprecated API usages with perfect accuracy, and update them correctly for 80.6% of the cases. We further improve the accuracy of MLCatchUp in performing updates by adding a feature that allows it to accept an additional user input that specifies the transformation constraints in the DSL for context-dependent API migration. Using this addition, MLCatchUp can make correct updates for 90.7% of the cases.

Tool Paper; International audience; Machine learning (ML) libraries are gaining vast popularity, especially in the Python programming language. Using the latest version of such libraries is recommended to ensure the best performance and security. When migrating to the latest version of a machine learning library, usages of deprecated APIs need to be updated, which is a time-consuming process. In this paper, we propose MLCatchUp, an automated API usage update tool for deprecated APIs of popular ML libraries written in Python. MLCatchUp automatically infers the required transformation to migrate usages of deprecated API through the differences between the deprecated and updated API signatures. MLCatchUp offers a readable transformation rule in the form of a domain specific language (DSL). We evaluate MLCatchUp using a dataset of 267 real-world Python code containing 551 usages of 68 distinct deprecated APIs, where MLCatchUp achieves 90.7% accuracy. A video demonstration of MLCatchUp is available at https://youtu.be/5NjOPNt5iaA.

Significance Cooperation is key to well-functioning groups and societies. Rather than addressing high-cost cooperation involving giving money or time and effort, we examine social mindfulness—a form of interpersonal benevolence that requires basic perspective-taking and is aimed at leaving choice for others. Do societies differ in social mindfulness, and if so, does it matter? Here, we find not only considerable variation across 31 nations and regions but also an association between social mindfulness and countries’ performance on environmental protection. We conclude that something as small and concrete as interpersonal benevolence can be entwined with current and future issues of global importance., Humans are social animals, but not everyone will be mindful of others to the same extent. Individual differences have been found, but would social mindfulness also be shaped by one’s location in the world? Expecting cross-national differences to exist, we examined if and how social mindfulness differs across countries. At little to no material cost, social mindfulness typically entails small acts of attention or kindness. Even though fairly common, such low-cost cooperation has received little empirical attention. Measuring social mindfulness across 31 samples from industrialized countries and regions (n = 8,354), we found considerable variation. Among selected country-level variables, greater social mindfulness was most strongly associated with countries’ better general performance on environmental protection. Together, our findings contribute to the literature on prosociality by targeting the kind of everyday cooperation that is more focused on communicating benevolence than on providing material benefits.

International audience; The Android operating system (OS) is often updated, where each new version may involve API deprecation. Usages of deprecated APIs in Android apps need to be updated to ensure the apps' compatibility with the old and new versions of the Android OS. In this work, we propose AndroEvolve, an automated tool to update usages of deprecated Android APIs, that addresses the limitations of the state-of-the-art tool, CocciEvolve. AndroEvolve utilizes data flow analysis to solve the problem of out-of-method-boundary variables, and variable denormalization to remove the temporary variables introduced by CocciEvolve. We evaluated the accuracy of AndroEvolve using a dataset of 360 target files and 20 deprecated Android APIs, where AndroEvolve is able to produce 319 correct updates, compared to CocciEvolve which only produces 249 correct updates. We also evaluated the readability of AndroEvolve's update results using a manual and an automatic evaluation. Both evaluations demonstrated that the code produced by AndroEvolve has higher readability than CocciEvolve's. A video demonstration of AndroEvolve is available at https://youtu.be/siU0tuMITXI

International audience; Digital maps such as Google Maps, Yelp, and Waze represent an incredible HCI success-they have transformed the way people navigate and access information about the world. However, there is a twofold problem limiting who can use these systems and how they benefit. First, these platforms focus almost exclusively on data about road networks and points of interest (POIs), noticeably lacking information about pedestrian infrastructure and physical accessibility. Second, because of their graphical nature and reliance on gesture and mouse input, digital maps can be inaccessible to some users-for example, those with visual or upper-body motor impairments. Thus, at a high level, there are two key accessibility problems related to accessible maps: 1) How can we collect, validate, and integrate accessibility information about the physical world into maps? 2) How can we design digital maps to be accessible to a diverse set of users across a wide range of physical, sensory, and cognitive abilities? Active research in HCI and beyond exists in both areas, but there has been no direct effort to unite this research community. To begin addressing this gap, we recently organized a Special Interest Group (SIG) at CHI2018 entitled "Making Maps Accessible and Putting Accessibility in Maps" (Figure 1). We set forth three explicit goals: First, to bring together and network scholars and practitioners who are broadly interested in accessible maps; second, to identify grand challenges and future research trajectories; and third, to establish accessible maps as a valuable topic within HCI. Accessibility is a broad, multifaceted topic. We assembled co-organizers from both academia and industry with varying topical expertise and regional and cultural experiences. The SIG attracted roughly 25 participants, including three telepresence robots, and interwove small-group brainstorming and discussion with large-group summary presentations. The two primary discussion topics were identifying key challenges and seeding potential solutions in the area of accessible maps. Below, we synthesize key themes and enumerate rich, open paths for future work, which emerged from the SIG (Table 1)

Global climate is changing as a result of anthropogenic warming, leading to higher daily excursions of temperature in cities. Such elevated temperatures have great implications on human thermal comfort and heat stress, which should be closely monitored. Current methods for heat exposure assessments (surveys, microclimate measurements, and laboratory experiments), however, present several limitations: measurements are scattered in time and space and data gathered on outdoor thermal stress and comfort often does not include physiological and behavioral parameters. To address these shortcomings, Project Coolbit aims to introduce a human-centric approach to thermal comfort assessments. In this study, we propose and evaluate the use of wrist-mounted wearable devices to monitor environmental and physiological responses that span a wide range of spatial and temporal distributions. We introduce an integrated wearable weather station that records (a) microclimate parameters (such as air temperature and humidity), (b) physiological parameters (heart rate, skin temperature and humidity), and (c) subjective feedback. The feasibility of this methodology to assess thermal comfort and heat stress is then evaluated using two sets of experiments: controlled-environment physiological data collection, and outdoor environmental data collection. We find that using the data obtained through the wrist-mounted wearables, core temperature can be predicted non-invasively with 95 percent of target attainment within ±0.27 °C. Additionally, a direct connection between the air temperature at the wrist (T a,w ) and the perceived activity level (PAV) of individuals was drawn. We observe that with increased T a,w , the desire for physical activity is significantly reduced, reaching ‘Transition only’ PAV level at 36 °C. These assessments reveal that the wearable methodology provides a comprehensive and accurate representation of human heat exposure, which can be extended in real-time to cover a large spatial distribution in a given city and quantify the impact of heat exposure on human life.

Due to the deprecation of APIs in the Android operating system,developers have to update usages of the APIs to ensure that their applications work for both the past and current versions of Android.Such updates may be widespread, non-trivial, and time-consuming. Therefore, automation of such updates will be of great benefit to developers. AppEvolve, which is the state-of-the-art tool for automating such updates, relies on having before- and after-update examples to learn from. In this work, we propose an approach named CocciEvolve that performs such updates using only a single after-update example. CocciEvolve learns edits by extracting the relevant update to a block of code from an after-update example. From preliminary experiments, we find that CocciEvolve can successfully perform 96 out of 112 updates, with a success rate of 85%., 5 pages, 8 figures. Accepted in The International Conference on Program Comprehension (ICPC) 2020, ERA Track

National audience; The decentralized partially observable Markov decisionprocess offers a unified framework for sequential decision-making by multiple collaborating agents but remains in-tractable. Mixed-integer linear formulations proved use-ful for partially observable domains, unfortunately ex-isting applications restrict to domains with one or twoagents. In this paper, we exploit a linearization propertythat allows us to reformulate nonlinear constraints fromn-agent settings into linear ones. We further present plan-ning and learning approaches relying on MILP formula-tions for general and special cases, including network-distributed and transition-independent problems. Experi-ments on standard2-agent benchmarks as well as domainswith a large number of agents provide strong empiricalsupport to the methodology.; Les processus décisionnels de Markov décentralises et partiellement observables (Dec-POMDPs) offrent un cadre unifie pour la prise de décisions séquentielles par de plusieurs agents collaboratifs—mais ils restent difficiles`a résoudre. Les reformulations en programmes linéaires mixtes (PLMs) se sont avérées utiles pour les processus décisionnels de Markov partiellement observables.Malheureusement, les applications existantes se limitent uniquement aux domaines mobilisant un ou deux agents. Dans cet article, nous exploitons une propriété de linéarisation qui nous permet de reformuler les contraintes non linéaires, omniprésentes dans les systèmes multi-agents, pour en faire des contraintes linéaires. Nous présentons en outre des approches de planification et d’apprentissage s’appuyant sur de nouvelles reformulations en PLMs des Dec-POMDPs, dans le cas général ainsi que quelques cas spécifiques. Les expérimentations sur des bancs de test standards`a deux et plus de deux agents fournissent un solide soutien`a cette méthodologie.

International audience; Existing work on software patches often use features specific to a single task. These works often rely on manually identified features, and human effort is required to identify these features for each task. In this work, we propose CC2Vec, a neural network model that learns a representation of code changes guided by their accompanying log messages, which represent the semantic intent of the code changes. CC2Vec models the hierarchical structure of a code change with the help of the attention mechanism and uses multiple comparison functions to identify the differences between the removed and added code.To evaluate if CC2Vec can produce a distributed representation of code changes that is general and useful for multiple tasks on software patches, we use the vectors produced by CC2Vec for three tasks: log message generation, bug fixing patch identification, and just-in-time defect prediction. In all tasks, the models using CC2Vec outperform the state-of-the-art techniques.

Service composition aims at achieving a business goal by composing existing service-based applications or components. The response time of a service is crucial especially in time critical business environments, which is often stated as a clause in service level agreements between service providers and service users. To meet the guaranteed response time requirement of a composite service, it is important to select a feasible set of component services such that their response time will collectively satisfy the response time requirement of the composite service. In this work, we use the BPEL modeling language, that aims at specifying Web services. We extend it with timing parameters, and equip it with a formal semantics. Then, we propose a fully automated approach to synthesize the response time requirement of component services modeled using BPEL, in the form of a constraint on the local response times. The synthesized requirement will guarantee the satisfaction of the global response time requirement, statically or dynamically. We implemented our work into a tool, Selamat, and performed several experiments to evaluate the validity of our approach., Comment: This is a pre-print of an article published in the International Journal on Software and Systems Modeling (SoSyM)

International audience; As the Android API evolves, some API methods may be deprecated, to be eventually removed. App developers face the challenge of keeping their apps up-to-date, to ensure that the apps work in both older and newer Android versions. Currently, AppEvolve is the state-of-the-art approach to automate such updates, and it has been shown to be quite effective. Still, the number of experiments reported is moderate, involving only API usage updates in 41 usage locations. In this work, we replicate the evaluation of AppEvolve and assess whether its effectiveness is generalizable. Given the set of APIs on which AppEvolve has been evaluated, we test AppEvolve on other mobile apps that use the same APIs. Our experiments show that AppEvolve fails to generate applicable updates for 81% of our dataset, even though the relevant knowledge for correct API updates is available in the examples. We first categorize the limitations of AppEvolve that lead to these failures. We then propose a mitigation strategy that solves 86% of these failures by a simple refactoring of the app code to better resemble the code in the examples. The refactoring usually involves assigning the target API method invocation and the arguments of the target API method into variables. Indeed, we have also seen such transformations in the dataset distributed with the AppEvolve replication package, as compared to the original source code from which this dataset is derived. Based on these findings, we propose some promising future directions.

International audience; Learning models from observations of a system is a powerful tool with many applications. In this paper, we consider learning Discrete Time Markov Chains (DTMC), with different methods such as frequency estimation or Laplace smoothing. While models learnt with such methods converge asymptotically towards the exact system, a more practical question in the realm of trusted machine learning is how accurate a model learnt with a limited time budget is. Existing approaches provide bounds on how close the model is to the original system, in terms of bounds on local (transition) probabilities, which has unclear implication on the global behavior. In this work, we provide global bounds on the error made by such a learning process, in terms of global behaviors formalized using temporal logic. More precisely, we propose a learning process ensuring a bound on the error in the probabilities of these properties. While such learning process cannot exist for the full LTL logic, we provide one ensuring a bound that is uniform over all the formulas of CTL. Further, given one time-to-failure property, we provide an improved learning algorithm. Interestingly, frequency estimation is sufficient for the latter, while Laplace smoothing is needed to ensure non-trivial uniform bounds for the full CTL logic.

International audience; Linux kernel stable versions serve the needs of users who value stability of the kernel over new features. The quality of such stable versions depends on the initiative of kernel developers and maintainers to propagate bug fixing patches to the stable versions. Thus, it is desirable to consider to what extent this process can be automated. A previous approach relies on words from commit messages and a small set of manually constructed code features. This approach, however, shows only moderate accuracy. In this paper, we investigate whether deep learning can provide a more accurate solution. We propose PatchNet, a hierarchical deep learning-based approach capable of automatically extracting features from commit messages and commit code and using them to identify stable patches. PatchNet contains a deep hierarchical structure that mirrors the hierarchical and sequential structure of commit code, making it distinctive from the existing deep learning models on source code. Experiments on 82,403 recent Linux patches confirm the superiority of PatchNet against various state-of-the-art baselines, including the one recently-adopted by Linux kernel maintainers.

Information leakage can have dramatic consequences on systems security. Among harmful information leaks, the timing information leakage is the ability for an attacker to deduce internal information depending on the system execution time. We address the following problem: given a timed system, synthesize the execution times for which one cannot deduce whether the system performed some secret behavior. We solve this problem in the setting of timed automata (TAs). We first provide a general solution, and then extend the problem to parametric TAs, by synthesizing internal timings making the TA secure. We study decidability, devise algorithms, and show that our method can also apply to program analysis., Comment: This is the author (and extended) version of the manuscript of the same name published in the proceedings of ATVA 2019. This work is partially supported by the ANR national research program PACS (ANR-14-CE28-0002), the ANR-NRF research program (ProMiS) and by ERATO HASUO Metamathematics for Systems Design Project (No. JPMJER1603), JST

Part 8: Pointing, Touch, Gesture and Speech-Based Interaction Techniques; International audience; Eyewear displays allow users to interact with virtual content displayed over real-world vision, in active situations like standing and walking. Pointing techniques for eyewear displays have been proposed, but their social acceptability, efficiency, and situation awareness remain to be assessed. Using a novel street-walking simulator, we conducted an empirical study of target acquisition while standing and walking under different levels of street crowdedness. We evaluated three phone-based eyewear pointing techniques: indirect touch on a touchscreen, and two in-air techniques using relative device rotations around forward and a downward axes. Direct touch on a phone, without eyewear, was used as a control condition. Results showed that indirect touch was the most efficient and socially acceptable technique, and that in-air pointing was inefficient when walking. Interestingly, the eyewear displays did not improve situation awareness compared to the control condition. We discuss implications for eyewear interaction design.

Developing software often requires code changes that are widespread and applied to multiple locations. There are tools for Java that allow developers to specify patterns for program matching and source-to-source transformation. However, to our knowledge, none allows for transforming code based on its control-flow context. We prototype Coccinelle4J, an extension to Coccinelle, which is a program transformation tool designed for widespread changes in C code, in order to work on Java source code. We adapt Coccinelle to be able to apply scripts written in the Semantic Patch Language (SmPL), a language provided by Coccinelle, to Java source files. As a case study, we demonstrate the utility of Coccinelle4J with the task of API migration. We show 6 semantic patches to migrate from deprecated Android API methods on several open source Android projects. We describe how SmPL can be used to express several API migrations and justify several of our design decisions.

International audience; This work proposes PatchNet, an automated tool based on hierarchical deep learning for classifying patches by extracting features from commit messages and code changes. PatchNet contains a deep hierarchical structure that mirrors the hierarchical and sequential structure of a code change, differentiating it from the existing deep learning models on source code. PatchNet provides several options allowing users to select parameters for the training process. The tool has been validated in the context of automatic identification of stable-relevant patches in the Linux kernel and is potentially applicable to automate other software engineering tasks that can be formulated as patch classification problems. A video demonstrating PatchNet is available at https://goo.gl/CZjG6X. The PatchNet implementation is available at https://github.com/hvdthong/PatchNetTool.

International audience; The chemical composition and properties of environmental media determine nanomaterial (NM) transport, fate, biouptake, and organism response. To compare and interpret experimental data, it is essential that sufficient context be provided for describing the physical and chemical characteristics of the setting in which a nanomaterial may be present. While the nanomaterial environmental, health and safety (NanoEHS) field has begun harmonization to allow data comparison and re-use (e.g. using standardized materials, defining a minimum set of required material characterizations), there is limited guidance for standardizing test media. Since most of the NM properties driving environmental behaviour and toxicity are medium-dependent, harmonization of media is critical. A workshop in March 2016 at Duke University identified five categories of test media: aquatic testing media, soil and sediment testing media, biological testing media, engineered systems testing media and product matrix testing media. For each category of test media, a minimum set of medium characteristics to report in all NM tests is recommended. Definitions and detail level of the recommendations for specific standardized media vary across these media categories. This reflects the variation in the maturity of their use as a test medium and associated measurement techniques, variation in utility and relevance of standardizing medium properties, ability to simplify standardizing reporting requirements, and in the availability of established standard reference media. Adoption of these media harmonization recommendations will facilitate the generation of integrated comparable datasets on NM fate and effects. This will in turn allow testing of the predictive utility of functional assay measurements on NMs in relevant media, support investigation of first principles approaches to understand behavioral mechanisms, and support categorization strategies to guide research, commercial development, and policy.

Voiceprint authentication provides great con- venience to users in many application scenarios. However, it easily suffers from spoofing attacks including speech synthesis, speech conversion and speech replay. Liveness detection is an effective way to resist these attacks. But existing methods suffer from many disadvantages, such as extra deployment costs due to precise data collection, environmental disturbance, high computational overhead and operational complexity. A uniform platform that can offer Voiceprint Authentication as a Service (VAaS) over the cloud is also lacked. Hence, it is imperative to design an economic and effective method for liveness detection in voiceprint authentication. In this paper, we propose a novel liveness detection method named LiVoAuth for voiceprint authentication. It applies a randomly generated vector se- quence as Liveness Detection Mode (LDM), corresponding to a random challenge code used for authentication. We implement LiVoAuth and conduct a series of user studies to evaluate its performance in terms of accuracy, stability, efficiency, security, and user acceptance. Experimental re- sults demonstrate its advantages compared with cutting- edge methods.

International audience; Testing is a pivotal activity in ensuring the quality of software. Code coverage is a common metric used as a yardstick to measure the efficacy and adequacy of testing. However, does higher coverage actually lead to a decline in post-release bugs? Do files that have higher test coverage actually have fewer bug reports? The direct relationship between code coverage and actual bug reports has not yet been analysed via a comprehensive empirical study on real bugs. Past studies only involve a few software systems or artificially injected bugs (mutants).In this empirical study, we examine these questions in the context of open-source software projects based on their actual reported bugs. We analyze 100 large open-source Java projects and measure the code coverage of the test cases that come along with these projects. We collect real bugs logged in the issue tracking system after the release of the software and analyse the correlations between code coverage and these bugs. We also collect other metrics such as cyclomatic complexity and lines of code, which are used to normalize the number of bugs and coverage to correlate with other metrics as well as use these metrics in regression analysis. Our results show that coverage has an insignificant correlation with the number of bugs that are found after the release of the software at the project level, and no such correlation at the file level.

International audience; Context: Code ownership metrics were recently defined in order to distinguish major and minor contributors of a software module, and to assess whether the ownership of such a module is strong or shared between developers. Objective: The relationship between these metrics and software quality was initially validated on proprietary software projects. Our objective in this paper is to evaluate such relationship in open-source software projects, and to compare these metrics to other code and process metrics. Method: On a newly crafted dataset of seven open-source software projects, we perform, using inferential statistics, an analysis of code ownership metrics and their relationship with software quality. Results: We confirm the existence of a relationship between code ownership and software quality, but the relative importance of ownership metrics in multiple linear regression models is low compared to metrics such as the number of lines of code, the number of modifications performed over the last release, or the number of developers of a module. Conclusion: Although we do find a relationship between code ownership and software quality, the added value of ownership metrics compared to other metrics is still to be proven.

International audience; Device drivers are essential components of any operating system (OS). They specify the communication protocol that allows the OS to interact with a device. However, drivers for new devices are usually created for a specific OS version. These drivers often need to be backported to the older versions to allow use of the new device. Backporting is often done manually, and is tedious and error prone. To alleviate this burden on developers, we propose an automatic recommendation system to guide the selection of backporting changes. Our approach analyzes the version history for cues to recommend candidate changes. We have performed an experiment on 100 Linux driver files and have shown that we can give a recommendation containing the correct backport for 68 of the drivers. For these 68 cases, 73.5%, 85.3%, and 88.2% of the correct recommendations are located in the Top-1, Top-2, and Top-5 positions of the recommendation lists respectively. The successful cases cover various kinds of changes including change of record access, deletion of function argument, change of a function name, change of constant, and change of if condition. Manual investigation of failed cases highlights limitations of our approach, including inability to infer complex changes, and unavailability of relevant cues in version history.

International audience; The external networks of directors significantly impact firm value and decisions. Surrounding close gubernatorial elections, local firms with directors connected to winners increase value by 4.1% over firms connected to losers. Director network’s value increases with network strength and activities, and is not due to network homophily. Connected firms are more likely to receive state subsidies, loans, and tax credits. They obtain better access to bank loans, borrow more, pay lower interest, invest and employ more, and enjoy better long-term performance. Network benefits are concentrated on connected firms, possibly through quid pro quo deals, and unlikely spread to industry competitors.

Management is not only taught in business schools. For more than 100 years it has also been taught by a special type of university that is ‘more than a business school’. An international group of university leaders trace the emergence, role and future contributions of ‘universities for business and management’

This paper examines how accounting quality affects the degree of debt concentration in corporate capital structures (i.e., a firm’s tendency to predominantly rely on only a few types of debt). Motivated by theoretical and empirical research that supports a strong link between creditors’ coordination costs and debt concentration and the importance of accounting quality in reducing these coordination costs, we hypothesize that firms with low accounting quality have a more concentrated debt structure. Measuring financial reporting quality by the disclosure of material internal control weaknesses over financial reporting (ICWs), we find that ICWs lead to a significantly more concentrated debt structure. We also show that the effect of ICWs on the degree of debt concentration is stronger for more severe ICW disclosures and for firms with a higher credit risk, further reinforcing the importance of financial reporting quality in determining debt concentration.

International audience; During software maintenance, testing is crucial to ensure the quality of code as it evolves. With the increasing size and complexity of software, adequate software testing has become increasingly important. Code coverage is an important metric to gauge the effectiveness of test cases and the adequacy of testing. However, what is the coverage level exhibited by large-scale open-source projects? What is the correlation between software metrics and the code coverage of the software?In this study, we investigate the state-of-the-practice of testing by measuring code coverage in open-source software projects. We examine over300 large open-source projects written in Java, coming from both the GitHub repository and the Debian Linux distribution, to measure the code coverage of their associated test cases. We analyse correlations between code coverage and relevant software metrics such as lines of code, cyclomatic complexity, and number of developers. We find that for the considered software developed under GitHub, average coverage is low, at 36%, and there is a significant negative correlation between coverage and both code size and complexity, i.e., coverage decreases with the increase in size and complexity of the software. The correlation between coverage and the number of developers is not significant. However, considering individual files in the GitHub software, coverage increases with the size and complexity, whereas the number of developers has no correlation with the code coverage. In contrast, the considered Debian software has a higher average rate of coverage, at 50%, but there is no correlation between coverage and any of the above metrics. Our results highlight the strengths and weaknesses of testing in open-source projects and make recommendations for future research.

International audience; Software developers and maintainers often need to locate code units responsible for a particular bug. A numberof Information Retrieval (IR) techniques have been proposed to map natural language bug descriptions to the associated code units. The vector space model (VSM) with the standard tf-idf weighting scheme (VSM natural ), has been shown to outperform nine other state-of-the-art IR techniques. However, there are multiple VSM variants with different weighting schemes, and their relative performance differs for different software systems.Based on this observation, we propose to compose various VSM variants, modelling their composition as an optimization problem. We propose a genetic algorithm (GA) based approach to explore the space of possible compositions and output a heuristically near-optimal composite model. We have evaluated our approach against several baselines on thousands of bug reports from AspectJ, Eclipse, and SWT. On average, our approach (VSM composite ) improves hit at 5 (Hit@5), mean average precision (MAP), and mean reciprocal rank (MRR) scores of VSM natural by 18.4%, 20.6%, and 10.5% respectively. We also integrate our compositional model with AmaLgam, which is a state-of-art bug localization technique. The resultant model named AmaLgam composite on average can improve Hit@5, MAP, and MRR scores of AmaLgam by 8.0%, 14.4% and 6.5% respectively.

International audience; Measuring the similarity of words is important in accurately representing and comparing documents, and thus improves the results of many natural language processing (NLP) tasks. The NLP community has proposed various measurements based on WordNet, a lexical database that contains relationships between many pairs of words. Recently, a number of techniques have been proposed to address software engineering issues such as code search and fault localization that require understanding natural language documents, and a measure of word similarity could improve their results. However, WordNet only contains information about words senses in general-purpose conversation, which often differ from word senses in a software-engineering context, and the software-specific word similarity resources that have been developed rely on data sources containing only a limited range of words and word uses.In recent work, we have proposed a word similarity resource based on information collected automatically from StackOverflow. We have found that the results of this resource are given scores on a 3-point Likert scale that are over 50% higher than the results of a resource based on WordNet. In this demo paper, we review our data collection methodology and propose a Java API to make the resulting word similarity resource useful in practice.The SEWordSim database and related information can be found at http://goo.gl/BVEAs8. Demo video is available at http://goo.gl/dyNwyb.

International audience; Many automated software engineering approaches, including code search, bug report categorization, and duplicatebug report detection, measure similarities between two documents by analyzing natural language contents. Often different words are used to express the same meaning and thus measuring similarities using exact matching of words is insufficient. To solve this problem, past studies have shown the need to measure the similarities between pairs of words. To meet this need, the natural language processing community has built WordNet which is a manually constructed lexical database that records semantic relations among words and can be used to measure how similar two words are. However, WordNet is a general purpose resource, and often does not contain software-specific words. Also, the meanings of words in WordNet are often different than when they are used in software engineering context. Thus, there is a need for a software-specific WordNet-like resource that can measure similarities of words.In this work, we propose an automated approach that builds a software-specific WordNet like resource, named WordSim-SE-DB, by leveraging the textual contents of posts in StackOverflow. Our approach measures the similarity of words by computing the similarities of the weighted co-occurrences of these words with three types of words in the textual corpus. We have evaluated our approach on a set of software-specific words and compared our approach with an existing WordNet-based technique (WordNet-res) to return top-k most similar words.Human judges are used to evaluate the effectiveness of the two techniques. We find that WordNet-res returns no result for 55% of the queries. For the remaining queries, WordNet-res returns significantly poorer results.

International audience; Developers often receive many feature requests. To implement these features, developers can leverage various methods from third party libraries. In this work, we propose an automated approach that takes as input a textual description of a feature request. It then recommends methods in library APIs that developers can use to implement the feature. Our recommendation approach learns from records of other changes made to software systems, and compares the textual description of the requested feature with the textual descriptions of various API methods. We have evaluated our approach on more than 500 feature requests of Axis2/Java, CXF, Hadoop Common, HBase, and Struts 2. Our experiments show that our approach is able to recommend the right methods from 10 libraries with an average recall-rate@5 of 0.690 and recall-rate@10 of 0.779 respectively. We also show that the state-of-the-art approach by Chan et al., that recommends API methods based on precise text phrases, is unable to handle feature requests.

International audience; Attempts have been made to understand driver development in terms of code clones. In this paper, we propose an alternate view, based on the metaphor of a gene. Guided by this metaphor, we study the structure of Linux 3.10 ethernet platform driver probe functions.

International audience; Feedback from software users constitutes a vital part in the evolution of software projects. By filing issue reports, users help identify and fix bugs, document software code, and enhance the software via feature requests. Many studies have explored issue reports, proposed approaches to enable the submission of higher-quality reports, and presented techniques to sort, categorize and leverage issues for software engineering needs. Who, however, cares about filing issues? What kind of issues are reported in issue trackers? What kind of correlation exist between issue reporting and the success of software projects? In this study, we address the need for answering such questions by performing an empirical study on a hundred thousands of open source projects. After filtering relevant trackers, the study used about 20,000 projects. We investigate and answer various research questions on the popularity and impact of issue trackers.

International audience; Many third party libraries are available to be downloaded and used. Using such libraries can reduce development time and make the developed software more reliable. However, developers are often unaware of suitable libraries to be used for their projects and thus they miss out on these benefits. To help developers better take advantage of the available libraries, we propose a new technique that automatically recommends libraries to developers. Our technique takes as input the set of libraries that an application currently uses, and recommends other libraries that are likely to be relevant. We follow a hybrid approach that combines association rule mining and collaborative filtering. The association rule mining component recommends libraries based on a set of library usage patterns. The collaborative filtering component recommends libraries based on those that are used by other similar projects. We investigate the effectiveness of our hybrid approach on 500 software projects that use many third-party libraries. Our experiments show that our approach can recommend libraries with recall rate@5 of 0.852 and recall rate@10 of 0.89

Funding Information: The work is supported in part by the Academy of Finland under Grants 308087, 335262, and 345072; in part by the National Natural Science Foundation of China under Grant 62072351; in part by the ZheJiang Lab; in part by the Shaanxi Innovation Team Project under Grant 2018TD-007; and in part by the 111 Project under Grant B16037. Publisher Copyright: © 2022 Association for Computing Machinery. As the sensitivity of position, the privacy protection in both 5G positioning and its further application in location-based services (LBSs) has been paid special attention and studied. Solutions based on k-anonymity, homomorphic encryption, and secure multi-party computation have been proposed. However, these solutions either require a trusted third party or incur heavy overheads. Besides, there still lacks an integrated solution that can protect privacy for both positioning and LBS provision. Based on Intel SGX, this article proposes a novel light-weight scheme that can protect privacy in both 5G positioning and its further applications in LBS provision in an integrated way. Through secret sharing, the proposed scheme can also support multiple location-based service providers without frequent key exchange. We seriously analyze the security of our scheme. Based on scheme implementation, its efficiency is proved through the performance evaluation conducted over a real-world database.

Coordination failure among owners of heterogeneous debt types increases distress costs. Covenants reduce expected distress costs by lowering the probability of liquidity shortages, increasing liquidation values, and incentivizing creditor monitoring. We predict and find that new debt contracts include more covenants when borrowers' existing debt structures are more heterogeneous. Our findings suggest that covenants are not only used to address creditor-shareholder conflicts but also to reduce the expected costs of coordination failure among creditors. Further, our results indicate a dynamic component missing from static debt structure models: Debt heterogeneity entails additional covenants (i.e., constraints) when raising future debt.

International audience; Programming languages have been proposed even before the era of the modern computer. As years have gone, computer resources have increased and application domains have expanded, leading to the proliferation of hundreds of programming languages, each attempting to improve over others or to address new programming paradigms. These languages range from procedural languages like C, object-oriented languages like Java, and functional languages such as ML and Haskell. Unfortunately, there is a lack of large scale and comprehensive studies that examine the "popularity", "interoperability", and "impact" of various programming languages. To fill this gap, this study investigates a hundred thousands of open source software projects to answer various research questions on the "popularity", "interoperability" and "impact" of various languages measured in different ways (e.g., in terms of lines of code, development teams, issues, etc.).

International audience; Software projects produce a wealth of data that is leveraged in different tasks and for different purposes: researchers collect project data for building experimental datasets; software programmers reuse code from projects; developers often explore the opportunities for getting involved in the development of a project to gain or offer expertise. Finding relevant projects that suit one needs is however currently challenging with the capabilities of existing search systems. We propose Orion, an integrated search engine architecture that combines information from different types of software repositories from multiple sources to facilitate the construction and execution of advanced search queries. Orion provides a declarative query language that gives to users access to a uniform interface where it transparently integrates different artifacts of project development and maintenance, such as source code information, version control systems metadata, bug tracking systems elements, and metadata on developer activities and interactions extracted from hosting platforms. We have built an extensible system with an initial capability of over 100,000 projects collected from the web, featuring several types of software repositories and software development artifacts. We conducted an experiment with 10 search scenarios to compare Orion with traditional search engines, and explore the need for our approach as well as the productivity of the proposed infrastructure. The results show with strong statistical significance that users find relevant projects faster and more accurately with Orion.

International audience; Social coding enables a different experience of software development as the activities and interests of one developer are easily advertized to other developers. Developers can thus track the activities relevant to various projects in one umbrella site. Such a major change in collaborative software development makes an investigation of networkings on social coding sites valuable. Furthermore, project hosting platforms promoting this development paradigm have been thriving, among which GitHub has arguably gained the most momentum. In this paper, we contribute to the body of knowledge on social coding by investigating the network structure of social coding in GitHub. We collect 100,000 projects and 30,000 developers from GitHub, construct developer-developer and project-project relationship graphs, and compute various characteristics of the graphs. We then identify influential developers and projects on this subnetwork of GitHub by using PageRank. Understanding how developers and projects are actually related to each other on a social coding site is the first step towards building tool supports to aid social programmers in performing their tasks more efficiently.

International audience; To collect software bugs found by users, development teams often setup bug trackers using systems such as Bugzilla. Developers would then fix some of the bugs and commit corresponding code changes into version control systems such as svn or git. Unfortunately, the links between bug reports and code changes are missing for many software projects as the bug tracking and version control systems are often maintained separately. Yet, linking bug reports to fix commits is important as it could shed light into the nature of bug fixing processes and expose patterns in software management. Bug linking solutions, such as ReLink, have been proposed. The demonstration of their effectiveness however faces a number of issues, including a reliability issue with their ground truth datasets as well as the extent of their measurements. We propose in this study a benchmark for evaluating bug linking solutions. This benchmark includes a dataset of about 12,000 bug links from 10 programs. These true links between bug reports and their fixes have been provided during bug fixing processes. We designed a number of research questions, to assess both quantitatively and qualitatively the effectiveness of a bug linking tool. Finally, we apply this benchmark on ReLink to report the strengths and limitations of this bug linking tool.

We examine how mark-to-market accounting affects the investment decisions of managers with reputation concerns. Reporting the current market value of a firm's assets can help mitigate agency problems because it provides outsiders (e.g., shareholders) with new information against which the management's decisions can be evaluated. However, the fact that the assets' market value is informative can also have a negative side effect: Managers may shy away from investments that indicate conflicting private information and would damage their reputation. This effect can lead to inefficient investment decisions and make marking to market less desirable when market prices are more informative.

International audience; In the evolution of an operating system there is a continuing tension between the need to develop and test new features, and the need to provide a stable and secure execution environment to users. A compromise, adopted by the developers of the Linux kernel, is to release new versions, including bug fixes and new features, frequently, while maintaining some older "longterm" versions. This strategy raises the problem of how to identify bug fixing patches that are submitted to the current version but should be applied to the longterm versions as well. The current approach is to rely on the individual subsystem maintainers to forward patches that seem relevant to the maintainers of the longterm kernels. The reactivity and diligence of the maintainers, however, varies, and thus many important patches could be missed by this approach. In this paper, we propose an approach that automatically identifies bug fixing patches based on the changes and commit messages recorded in code repositories. We compare our approach with the keyword-based approach for identifying bug-fixing patches used in the literature, in the context of the Linux kernel. The results show that our approach can achieve a 53.19% improvement in recall as compared to keyword-based approaches, with similar precision.

Information leakage can have dramatic consequences on systems security. Among harmful information leaks, the timing information leakage occurs whenever an attacker successfully deduces confidential internal information. In this work, we consider that the attacker has access (only) to the system execution time. We address the following timed opacity problem: given a timed system, a private location and a final location, synthesize the execution times from the initial location to the final location for which one cannot deduce whether the system went through the private location. We also consider the full timed opacity problem, asking whether the system is opaque for all execution times. We show that these problems are decidable for timed automata (TAs) but become undecidable when one adds parameters, yielding parametric timed automata (PTAs). We identify a subclass with some decidability results. We then devise an algorithm for synthesizing PTAs parameter valuations guaranteeing that the resulting TA is opaque. We finally show that our method can also apply to program analysis., This is the author version of the manuscript of the same name published in ACM Transactions on Software Engineering and Methodology (ToSEM). This work is partially supported by the ANR national research program PACS (ANR-14-CE28-0002), by the ANR-NRF research program ProMiS (ANR-19-CE25-0015), and by ERATO HASUO Metamathematics for Systems Design Project (No. JPMJER1603), JST. arXiv admin note: substantial text overlap with arXiv:1907.00537

International audience; The Android operating system is frequently updated, with each version bringing a new set of APIs. New versions may involve API deprecation; Android apps using deprecated APIs need to be updated to ensure the apps' compatibility with old and new Android versions. Updating deprecated APIs is a time-consuming endeavor. Hence, automating the updates of Android APIs can be beneficial for developers. CocciEvolve is the state-of-the-art approach for this automation. However, it has several limitations, including its inability to resolve out-of-method variables and the low code readability of its updates due to the addition of temporary variables. In an attempt to further improve the performance of automated Android API update, we propose an approach named AndroEvolve, that addresses the limitations of CocciEvolve through the addition of data flow analysis and variable name denormalization. Data flow analysis enables AndroEvolve to resolve the value of any variable within the file scope. Variable name denormalization replaces temporary variables that may present in the CocciEvolve update with appropriate values in the target file. We have evaluated the performance of AndroEvolve and the readability of its updates on 372 target files containing 565 deprecated API usages. Each target file represents a file from an Android application that uses a deprecated API in its code. AndroEvolve successfully updates 481 out of 565 deprecated API invocations correctly, achieving an accuracy of 85.1%. Compared to CocciEvolve, AndroEvolve produces 32.9% more instances of correct updates. Moreover, our manual and automated evaluation shows that AndroEvolve updates are more readable than CocciEvolve updates.

Encrypted data deduplication, along with different preferences in data access control, brings the birth of hybrid encrypted cloud data deduplication (H-DEDU for short). However, whether H-DEDU can be successfully deployed in practice has not been seriously investigated. Obviously, the adoption of H-DEDU depends on whether it can bring economic benefits to all stakeholders. But existing economic models of cloud storage fail to support H-DEDU due to complicated interactions among stakeholders. In this paper, we establish a formal economic model of H-DEDU by formulating the utilities of all involved stakeholders, i.e., data holders, data owners, and Cloud Storage Providers (CSPs). Then, we construct a multi-stage Stackelberg game, which consists of Holder Participation Game, Owner Online Game and CSP Pricing Game, to capture the interactions among all system stakeholders. We further analyze the conditions of the existence of a sub-game perfect Nash Equilibrium and propose a gradient-based algorithm to help the stakeholders choose near-optimal strategies. Extensive experiments show the feasibility of the proposed algorithm in achieving the Nash Equilibrium of the Stackelberg game. Additionally, we investigate the effects of parameters related to CSP, data owners and data holders on H-DEDU adoption. Our study advises all stakeholders the best strategies to adopt H-DEDU.

International audience; Security of web servers has become a sensitive subject today. Prediction of normal and abnormal request is problematic due to large number of false alarms in many anomaly based Intrusion Detection Systems(IDS). SS-IDS derives automatical ly the parameter profiles from the analyzed data thereby generating the Statistical Signatures. Statistical Signatures are based on modeling of normal requests and their distribution value without explicit intervention. Several attributes are used to calculate the behavior of the legitimate request on the web server. SS-IDS is best suited for the newly instal led web servers which doesn't have large number of requests in the data set to train the IDS and can be used on top of currently used signature based IDS like SNORT. Experiments conducted on real data sets have shown high accuracy up to 99.98% for predicting valid request as valid and false positive rate ranges from 3.82-7.84%.

Cahier de Recherche du Groupe HEC 925; This paper re-examines the incentives of mutual fund managers arising from investor flows. We provide evidence that the convexity of the flow-performance relationship varies with economic activity. We show that the effect is economically large and is not driven by abnormal years. We test two possible channels through which this pattern may arise. We investigate implications of the time-varying convexity for the incentives of managers to alter strategically the risk of their portfolios. We provide evidence that poor mid-year performers increase the risk of the portfolio only when economic activity is strong. Finally, we briefly discuss some methodological implications.

We offer new evidence on the link between price points and price rigidity using two datasets. One is a large weekly transaction price dataset, covering 29 product categories over an eight-year period from a large U.S. supermarket chain. The other is from the Internet, and includes daily prices over a two-year period for 474 consumer electronic goods covering ten product categories, from 293 different Internet retailers. Across the two datasets, we find that (i) 9 is the most frequently used price-ending for the penny, dime, dollar and the ten-dollar digits, (ii) the most common price changes are in multiples of dimes, dollars, and ten-dollars, (iii) 9-ending prices are at least 24% (and as much as 73%) less likely to change in comparison to prices ending with other digits, and (iv) the average size of the price change is higher if the price ends with 9 in comparison to non-9-ending prices. This link between price points and price rigidity is robust across a wide range of prices, products, product categories, and retail formats. We offer a behavioral explanation for the findings.