References of "Klein, Jacques 50002098"
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See detailComparing MultiLingual and Multiple MonoLingual Models for Intent Classification and Slot Filling
Lothritz, Cedric UL; Allix, Kevin UL; Lebichot, Bertrand UL et al

in 26th International Conference on Applications of Natural Language to Information Systems (2021, June 25)

With the momentum of conversational AI for enhancing client-to-business interactions, chatbots are sought in various domains, including FinTech where they can automatically handle requests for opening ... [more ▼]

With the momentum of conversational AI for enhancing client-to-business interactions, chatbots are sought in various domains, including FinTech where they can automatically handle requests for opening/closing bank accounts or issuing/terminating credit cards. Since they are expected to replace emails and phone calls, chatbots must be capable to deal with diversities of client populations. In this work, we focus on the variety of languages, in particular in multilingual countries. Specifically, we investigate the strategies for training deep learning models of chatbots with multilingual data. We perform experiments for the specific tasks of Intent Classification and Slot Filling in financial domain chatbots and assess the performance of mBERT multilingual model vs multiple monolingual models. [less ▲]

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See detailRAICC: Revealing Atypical Inter-Component Communication in Android Apps
Samhi, Jordan UL; Bartel, Alexandre UL; Bissyande, Tegawendé François D Assise UL et al

in Samhi, Jordan; Bartel, Alexandre; Bissyande, Tegawendé François D Assise (Eds.) et al 43rd International Conference on Software Engineering (ICSE) (2021, May)

Inter-Component Communication (ICC) is a key mechanism in Android. It enables developers to compose rich functionalities and explore reuse within and across apps. Unfortunately, as reported by a large ... [more ▼]

Inter-Component Communication (ICC) is a key mechanism in Android. It enables developers to compose rich functionalities and explore reuse within and across apps. Unfortunately, as reported by a large body of literature, ICC is rather "complex and largely unconstrained", leaving room to a lack of precision in apps modeling. To address the challenge of tracking ICCs within apps, state of the art static approaches such as Epicc, IccTA and Amandroid have focused on the documented framework ICC methods (e.g., startActivity) to build their approaches. In this work we show that ICC models inferred in these state of the art tools may actually be incomplete: the framework provides other atypical ways of performing ICCs. To address this limitation in the state of the art, we propose RAICC a static approach for modeling new ICC links and thus boosting previous analysis tasks such as ICC vulnerability detection, privacy leaks detection, malware detection, etc. We have evaluated RAICC on 20 benchmark apps, demonstrating that it improves the precision and recall of uncovered leaks in state of the art tools. We have also performed a large empirical investigation showing that Atypical ICC methods are largely used in Android apps, although not necessarily for data transfer. We also show that RAICC increases the number of ICC links found by 61.6% on a dataset of real-world malicious apps, and that RAICC enables the detection of new ICC vulnerabilities. [less ▲]

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See detailLes dangers de pastebin
Samhi, Jordan UL; Bissyande, Tegawendé François D Assise UL; Klein, Jacques UL

Article for general public (2021)

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See detailA Comparison of Pre-Trained Language Models for Multi-Class Text Classification in the Financial Domain
Arslan, Yusuf UL; Allix, Kevin UL; Veiber, Lisa UL et al

in Companion Proceedings of the Web Conference 2021 (WWW '21 Companion), April 19--23, 2021, Ljubljana, Slovenia (2021, April 19)

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See detailRevisiting the VCCFinder approach for the identification of vulnerability-contributing commits
Riom, Timothée UL; Sawadogo, Delwende Donald Arthur UL; Allix, Kevin UL et al

in Empirical Software Engineering (2021), 26

Detecting vulnerabilities in software is a constant race between development teams and potential attackers. While many static and dynamic approaches have focused on regularly analyzing the software in its ... [more ▼]

Detecting vulnerabilities in software is a constant race between development teams and potential attackers. While many static and dynamic approaches have focused on regularly analyzing the software in its entirety, a recent research direction has focused on the analysis of changes that are applied to the code. VCCFinder is a seminal approach in the literature that builds on machine learning to automatically detect whether an incoming commit will introduce some vulnerabilities. Given the influence of VCCFinder in the literature, we undertake an investigation into its performance as a state-of-the-art system. To that end, we propose to attempt a replication study on the VCCFinder supervised learning approach. The insights of our failure to replicate the results reported in the original publication informed the design of a new approach to identify vulnerability-contributing commits based on a semi-supervised learning technique with an alternate feature set. We provide all artefacts and a clear description of this approach as a new reproducible baseline for advancing research on machine learning-based identification of vulnerability-introducing commits [less ▲]

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See detailA critical review on the evaluation of automated program repair systems
Kui, Liu; Li, Li; Koyuncu, Anil UL et al

in Journal of Systems and Software (2021)

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See detailLessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection
Daoudi, Nadia UL; Allix, Kevin UL; Bissyande, Tegawendé François D Assise UL et al

in Empirical Software Engineering (2021), 26

A well-known curse of computer security research is that it often produces systems that, while technically sound, fail operationally. To overcome this curse, the community generally seeks to assess ... [more ▼]

A well-known curse of computer security research is that it often produces systems that, while technically sound, fail operationally. To overcome this curse, the community generally seeks to assess proposed systems under a variety of settings in order to make explicit every potential bias. In this respect, recently, research achievements on machine learning based malware detection are being considered for thorough evaluation by the community. Such an effort of comprehensive evaluation supposes first and foremost the possibility to perform an independent reproduction study in order to sharpen evaluations presented by approaches’ authors. The question Can published approaches actually be reproduced? thus becomes paramount despite the little interest such mundane and practical aspects seem to attract in the malware detection field. In this paper, we attempt a complete reproduction of five Android Malware Detectors from the literature and discuss to what extent they are “reproducible”. Notably, we provide insights on the implications around the guesswork that may be required to finalise a working implementation. Finally, we discuss how barriers to reproduction could be lifted, and how the malware detection field would benefit from stronger reproducibility standards—like many various fields already have. [less ▲]

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See detailA First Look at Android Applications in Google Play related to Covid-19
Samhi, Jordan UL; Allix, Kevin UL; Bissyande, Tegawendé François D Assise UL et al

in Empirical Software Engineering (2021)

Due to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the Covid-19 pandemic, app developers ... [more ▼]

Due to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the Covid-19 pandemic, app developers have joined the response effort in various ways by releasing apps that target different user bases (e.g., all citizens or journalists), offer different services (e.g., location tracking or diagnostic-aid), provide generic or specialized information, etc. While many apps have raised some concerns by spreading misinformation or even malware, the literature does not yet provide a clear landscape of the different apps that were developed. In this study, we focus on the Android ecosystem and investigate Covid-related Android apps. In a best-effort scenario, we attempt to systematically identify all relevant apps and study their characteristics with the objective to provide a First taxonomy of Covid related apps, broadening the relevance beyond the implementation of contact tracing. Overall, our study yields a number of empirical insights that contribute to enlarge the knowledge on Covid-related apps: (1) Developer communities contributed rapidly to the Covid-19, with dedicated apps released as early as January 2020; (2) Covid-related apps deliver digital tools to users (e.g., health diaries), serve to broadcast information to users (e.g., spread statistics), and collect data from users (e.g., for tracing); (3) Covid-related apps are less complex than standard apps; (4) they generally do not seem to leak sensitive data; (5) in the majority of cases, Covid-related apps are released by entities with past experience on the market, mostly official government entities or public health organizations. [less ▲]

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See detailDexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection Based on Image Representation of Bytecode
Daoudi, Nadia UL; Samhi, Jordan UL; Kabore, Abdoul Kader UL et al

in Communications in Computer and Information Science (2021)

Computer vision has witnessed several advances in recent years, with unprecedented performance provided by deep representation learning research. Image formats thus appear attractive to other fields such ... [more ▼]

Computer vision has witnessed several advances in recent years, with unprecedented performance provided by deep representation learning research. Image formats thus appear attractive to other fields such as malware detection, where deep learning on images alleviates the need for comprehensively hand-crafted features generalising to different malware variants. We postulate that this research direction could become the next frontier in Android malware detection, and therefore requires a clear roadmap to ensure that new approaches indeed bring novel contributions. We contribute with a first building block by developing and assessing a baseline pipeline for image-based malware detection with straightforward steps. We propose DexRay, which converts the bytecode of the app DEX files into grey-scale “vector” images and feeds them to a 1-dimensional Convolutional Neural Network model. We view DexRay as foundational due to the exceedingly basic nature of the design choices, allowing to infer what could be a minimal performance that can be obtained with image-based learning in malware detection. The performance of DexRay evaluated on over 158k apps demonstrates that, while simple, our approach is effective with a high detection rate(F1-score= 0.96). Finally, we investigate the impact of time decay and image-resizing on the performance of DexRay and assess its resilience to obfuscation. This work-in-progress paper contributes to the domain of Deep Learning based Malware detection by providing a sound, simple, yet effective approach (with available artefacts) that can be the basis to scope the many profound questions that will need to be investigated to fully develop this domain. [less ▲]

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See detailEvaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition
Lothritz, Cedric UL; Allix, Kevin UL; Veiber, Lisa UL et al

in Proceedings of the 28th International Conference on Computational Linguistics (2020, December)

Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task and has remained an active research field. In recent years, transformer models and more specifically the BERT model ... [more ▼]

Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task and has remained an active research field. In recent years, transformer models and more specifically the BERT model developed at Google revolutionised the field of NLP. While the performance of transformer-based approaches such as BERT has been studied for NER, there has not yet been a study for the fine-grained Named Entity Recognition (FG-NER) task. In this paper, we compare three transformer-based models (BERT, RoBERTa, and XLNet) to two non-transformer-based models (CRF and BiLSTM-CNN-CRF). Furthermore, we apply each model to a multitude of distinct domains. We find that transformer-based models incrementally outperform the studied non-transformer-based models in most domains with respect to the F1 score. Furthermore, we find that the choice of domains significantly influenced the performance regardless of the respective data size or the model chosen. [less ▲]

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See detailBorrowing your enemy's arrows: the case of code reuse in android via direct inter-app code invocation
Gao, Jun UL; li, li; Kong, Pingfan UL et al

in ESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (2020, November)

{The Android ecosystem offers different facilities to enable communication among app components and across apps to ensure that rich services can be composed through functionality reuse. At the heart of ... [more ▼]

{The Android ecosystem offers different facilities to enable communication among app components and across apps to ensure that rich services can be composed through functionality reuse. At the heart of this system is the Inter-component communication (ICC) scheme, which has been largely studied in the literature. Less known in the community is another powerful mechanism that allows for direct inter-app code invocation which opens up for different reuse scenarios, both legitimate or malicious. This paper exposes the general workflow for this mechanism, which beyond ICCs, enables app developers to access and invoke functionalities (either entire Java classes, methods or object fields) implemented in other apps using official Android APIs. We experimentally showcase how this reuse mechanism can be leveraged to â plagiarize" supposedly-protected functionalities. Typically, we were able to leverage this mechanism to bypass security guards that a popular video broadcaster has placed for preventing access to its video database from outside its provided app. We further contribute with a static analysis toolkit, named DICIDer, for detecting direct inter-app code invocations in apps. An empirical analysis of the usage prevalence of this reuse mechanism is then conducted. Finally, we discuss the usage contexts as well as the implications of this studied reuse mechanism [less ▲]

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See detailData-driven simulation and optimization for covid-19 exit strategies
Ghamizi, Salah UL; Rwemalika, Renaud UL; Cordy, Maxime UL et al

in Ghamizi, Salah; Rwemalika, Renaud; Cordy, Maxime (Eds.) et al Data-driven simulation and optimization for covid-19 exit strategies (2020, August)

The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive ... [more ▼]

The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive lockdown of entire countries and cities, which beyond its global economic impact has created some deep social and psychological tensions within populations. While the adopted mitigation measures (including the lockdown) have generally proven useful, policymakers are now facing a critical question: how and when to lift the mitigation measures? A carefully-planned exit strategy is indeed necessary to recover from the pandemic without risking a new outbreak. Classically, exit strategies rely on mathematical modeling to predict the effect of public health interventions. Such models are unfortunately known to be sensitive to some key parameters, which are usually set based on rules-of-thumb.In this paper, we propose to augment epidemiological forecasting with actual data-driven models that will learn to fine-tune predictions for different contexts (e.g., per country). We have therefore built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease in order to predict the cases and deaths, and a genetic algorithm component searching for optimal trade-offs/policies between constraints and objectives set by decision-makers.Replaying pandemic evolution in various countries, we experimentally show that our approach yields predictions with much lower error rates than pure epidemiological models in 75% of the cases and achieves a 95% R² score when the learning is transferred and tested on unseen countries. When used for forecasting, this approach provides actionable insights into the impact of individual measures and strategies. [less ▲]

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See detailChallenges Towards Production-Ready Explainable Machine Learning
Veiber, Lisa UL; Allix, Kevin UL; Arslan, Yusuf UL et al

in Veiber, Lisa; Allix, Kevin; Arslan, Yusuf (Eds.) et al Proceedings of the 2020 USENIX Conference on Operational Machine Learning (OpML 20) (2020, July)

Machine Learning (ML) is increasingly prominent in or- ganizations. While those algorithms can provide near perfect accuracy, their decision-making process remains opaque. In a context of accelerating ... [more ▼]

Machine Learning (ML) is increasingly prominent in or- ganizations. While those algorithms can provide near perfect accuracy, their decision-making process remains opaque. In a context of accelerating regulation in Artificial Intelligence (AI) and deepening user awareness, explainability has become a priority notably in critical healthcare and financial environ- ments. The various frameworks developed often overlook their integration into operational applications as discovered with our industrial partner. In this paper, explainability in ML and its relevance to our industrial partner is presented. We then dis- cuss the main challenges to the integration of ex- plainability frameworks in production we have faced. Finally, we provide recommendations given those challenges. [less ▲]

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See detailOn the Efficiency of Test Suite based Program Repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs
Liu, Kui UL; Wang, Shangwen; Koyuncu, Anil UL et al

in 42nd ACM/IEEE International Conference on Software Engineering (ICSE) (2020, May)

Test-based automated program repair has been a prolific field of research in software engineering in the last decade. Many approaches have indeed been proposed, which leverage test suites as a weak, but ... [more ▼]

Test-based automated program repair has been a prolific field of research in software engineering in the last decade. Many approaches have indeed been proposed, which leverage test suites as a weak, but affordable, approximation to program specifications. Although the literature regularly sets new records on the number of benchmark bugs that can be fixed, several studies increasingly raise concerns about the limitations and biases of state-of-the-art approaches. For example, the correctness of generated patches has been questioned in a number of studies, while other researchers pointed out that evaluation schemes may be misleading with respect to the processing of fault localization results. Nevertheless, there is little work addressing the efficiency of patch generation, with regard to the practicality of program repair. In this paper, we fill this gap in the literature, by providing an extensive review on the efficiency of test suite based program repair. Our objective is to assess the number of generated patch candidates, since this information is correlated to (1) the strategy to traverse the search space efficiently in order to select sensical repair attempts, (2) the strategy to minimize the test effort for identifying a plausible patch, (3) as well as the strategy to prioritize the generation of a correct patch. To that end, we perform a large-scale empirical study on the efficiency, in terms of quantity of generated patch candidates of the 16 open-source repair tools for Java programs. The experiments are carefully conducted under the same fault localization configurations to limit biases. Eventually, among other findings, we note that: (1) many irrelevant patch candidates are generated by changing wrong code locations; (2) however, if the search space is carefully triaged, fault localization noise has little impact on patch generation efficiency; (3) yet, current template-based repair systems, which are known to be most effective in fixing a large number of bugs, are actually least efficient as they tend to generate majoritarily irrelevant patch candidates. [less ▲]

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See detailMadDroid: Characterizing and Detecting Devious Ad Contents for Android Apps
Liu, Tianming; Wang, Haoyu; Li, Li et al

in Proceedings of The Web Conference 2020 (2020, April)

Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, which poses a number of ... [more ▼]

Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, which poses a number of threats, e.g., propagating malware and undesirable contents. To understand the practice of these devious ad behaviors, we perform a large-scale study on the app contents harvested through automated app testing. In this work, we first provide a comprehensive categorization of devious ad contents, including five kinds of behaviors belonging to two categories: ad loading content and ad clicking content. Then, we propose MadDroid, a framework for automated detection of devious ad contents. MadDroid leverages an automated app testing framework with a sophisticated ad view exploration strategy for effectively collecting ad-related network traffic and subsequently extracting ad contents. We then integrate dedicated approaches into the framework to identify devious ad contents. We have applied MadDroid to 40,000 Android apps and found that roughly 6% of apps deliver devious ad contents, e.g., distributing malicious apps that cannot be downloaded via traditional app markets. Experiment results indicate that devious ad contents are prevalent, suggesting that our community should invest more effort into the detection and mitigation of devious ads towards building a trustworthy mobile advertising ecosystem. [less ▲]

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See detailFixMiner: Mining relevant fix patterns for automated program repair
Koyuncu, Anil UL; Liu, Kui UL; Bissyande, Tegawendé François D Assise UL et al

in Empirical Software Engineering (2020)

Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurrence of bugs across ... [more ▼]

Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurrence of bugs across projects, the associated similar patches can be leveraged to extract generic fix actions. While the literature includes various approaches leveraging similarity among patches to guide program repair, these approaches often do not yield fix patterns that are tractable and reusable as actionable input to APR systems. In this paper, we propose a systematic and automated approach to mining relevant and actionable fix patterns based on an iterative clustering strategy applied to atomic changes within patches. The goal of FixMiner is thus to infer separate and reusable fix patterns that can be leveraged in other patch generation systems. Our technique, FixMiner, leverages Rich Edit Script which is a specialized tree structure of the edit scripts that captures the ASTlevel context of the code changes. FixMiner uses different tree representations of Rich Edit Scripts for each round of clustering to identify similar changes. These are abstract syntax trees, edit actions trees, and code context trees. We have evaluated FixMiner on thousands of software patches collected from open source projects. Preliminary results show that we are able to mine accurate patterns, efficiently exploiting change information in Rich Edit Scripts. We further integrated the mined patterns to an automated program repair prototype, PARFixMiner, with which we are able to correctly fix 26 bugs of the Defects4J benchmark. Beyond this quantitative performance, we show that the mined fix patterns are sufficiently relevant to produce patches with a high probability of correctness: 81% of PARFixMiner’s generated plausible patches are correct. [less ▲]

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See detailCDA: Characterising Deprecated Android APIs
li, li; Gao, Jun UL; Bissyande, Tegawendé François D Assise UL et al

in Empirical Software Engineering (2020), 24(118), 1-41

Because of functionality evolution, or security and performance-related changes, some APIs eventually become unnecessary in a software system and thus need to be cleaned to ensure proper maintainability ... [more ▼]

Because of functionality evolution, or security and performance-related changes, some APIs eventually become unnecessary in a software system and thus need to be cleaned to ensure proper maintainability. Those APIs are typically marked first as deprecated APIs and, as recommended, follow through a deprecated-replace-remove cycle, giving an opportunity to client application developers to smoothly adapt their code in next updates. Such a mechanism is adopted in the Android framework development where thousands of reusable APIs are made available to Android app developers. In this work, we present a research-based prototype tool called CDA and apply it to different revisions (i.e., releases or tags) of the Android framework code for characterising deprecated APIs. Based on the data mined by CDA, we then perform an empirical study on API deprecation in the Android ecosystem and the associated challenges for maintaining quality apps. In particular, we investigate the prevalence of deprecated APIs, their annotations and documentation, their removal and consequences, their replacement messages, developer reactions to API deprecation, as well as the evolution of the usage of deprecated APIs. Experimental results reveal several findings that further provide promising insights related to deprecated Android APIs. Notably, by mining the source code of the Android framework base, we have identified three bugs related to deprecated APIs. These bugs have been quickly assigned and positively appreciated by the framework maintainers, who claim that these issues will be updated in future releases. [less ▲]

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See detailUnderstanding the Evolution of Android App Vulnerabilities
Gao, Jun UL; li, li; Bissyande, Tegawendé François D Assise UL et al

in IEEE Transactions on Reliability (2020)

The Android ecosystem today is a growing universe of a few billion devices, hundreds of millions of users and millions of applications targeting a wide range of activities where sensitive information is ... [more ▼]

The Android ecosystem today is a growing universe of a few billion devices, hundreds of millions of users and millions of applications targeting a wide range of activities where sensitive information is collected and processed. Security of communication and privacy of data are thus of utmost importance in application development. Yet, regularly, there are reports of successful attacks targeting Android users. While some of those attacks exploit vulnerabilities in the Android OS, others directly concern application-level code written by a large pool of developers with varying experience. Recently, a number of studies have investigated this phenomenon, focusing however only on a specific vulnerability type appearing in apps, and based on only a snapshot of the situation at a given time. Thus, the community is still lacking comprehensive studies exploring how vulnerabilities have evolved over time, and how they evolve in a single app across developer updates. Our work fills this gap by leveraging a data stream of 5 million app packages to re-construct versioned lineages of Android apps and finally obtained 28;564 app lineages (i.e., successive releases of the same Android apps) with more than 10 app versions each, corresponding to a total of 465;037 apks. Based on these app lineages, we apply state-of- the-art vulnerability-finding tools and investigate systematically the reports produced by each tool. In particular, we study which types of vulnerabilities are found, how they are introduced in the app code, where they are located, and whether they foreshadow malware. We provide insights based on the quantitative data as reported by the tools, but we further discuss the potential false positives. Our findings and study artifacts constitute a tangible knowledge to the community. It could be leveraged by developers to focus verification tasks, and by researchers to drive vulnerability discovery and repair research efforts. [less ▲]

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See detailEvaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair
Tian, Haoye UL; Liu, Kui UL; Kaboreé, Abdoul Kader et al

in Tian, Haoye (Ed.) 35th IEEE/ACM International Conference on Automated Software Engineering, September 21-25, 2020, Melbourne, Australia (2020)

A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect ... [more ▼]

A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect, the generated patches, although validated by the oracle, may actually be incorrect. While the state of the art explore research directions that require dynamic information or rely on manually-crafted heuristics, we study the benefit of learning code representations to learn deep features that may encode the properties of patch correctness. Our work mainly investigates different representation learning approaches for code changes to derive embeddings that are amenable to similarity computations. We report on findings based on embeddings produced by pre-trained and re-trained neural networks. Experimental results demonstrate the potential of embeddings to empower learning algorithms in reasoning about patch correctness: a machine learning predictor with BERT transformer-based embeddings... [less ▲]

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See detailHandling duplicates in Dockerfiles families: Learning from experts
Oumaziz, Mohamed; Falleri, Jean-Rémy; Blanc, Xavier et al

in 35th IEEE International Conference on Software Maintenance and Evolution (ICSME) (2019, October)

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