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See detailComparison of metadata quality in open data portals using the Analytic Hierarchy Process
Kubler, sylvain; Robert, Jérémy UL; Neumaier, Sebastian et al

in Government Information Quarterly (2018)

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See detailAugmenting and Structuring User Queries to Support Efficient Free-Form Code Search
Sirres, Raphael; Bissyande, Tegawendé François D Assise UL; Kim, Dongsun et al

in Empirical Software Engineering (2018), 90

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See detailPredicting the Fault Revelation Utility of Mutants
Titcheu Chekam, Thierry UL; Papadakis, Mike UL; Bissyande, Tegawendé François D Assise UL et al

in 40th International Conference on Software Engineering, Gothenburg, Sweden, May 27 - 3 June 2018 (2018)

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See detailMutant Quality Indicators
Papadakis, Mike UL; Titcheu Chekam, Thierry UL; Le Traon, Yves UL

in 13th International Workshop on Mutation Analysis (MUTATION'18) (2018)

Detailed reference viewed: 268 (20 UL)
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See detailFini le Bac à Sable. Avec le CVE-2017-3272, devenez un grand!
Bartel, Alexandre UL; Klein, Jacques UL; Le Traon, Yves UL

Article for general public (2018)

Detailed reference viewed: 94 (6 UL)
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See detailDésérialisation Java : Une brève introduction
Bartel, Alexandre UL; Klein, Jacques UL; Le Traon, Yves UL

Article for general public (2018)

Detailed reference viewed: 96 (9 UL)
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See detailEnabling the Continous Analysis of Security Vulnerabilities with VulData7
Jimenez, Matthieu UL; Le Traon, Yves UL; Papadakis, Mike UL

in IEEE International Working Conference on Source Code Analysis and Manipulation (2018)

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See detailMining Fix Patterns for FindBugs Violations
Liu, Kui UL; Kim, Dongsun; Bissyande, Tegawendé François D Assise UL et al

in IEEE Transactions on Software Engineering (2018)

Several static analysis tools, such as Splint or FindBugs, have been proposed to the software development community to help detect security vulnerabilities or bad programming practices. However, the ... [more ▼]

Several static analysis tools, such as Splint or FindBugs, have been proposed to the software development community to help detect security vulnerabilities or bad programming practices. However, the adoption of these tools is hindered by their high false positive rates. If the false positive rate is too high, developers may get acclimated to violation reports from these tools, causing concrete and severe bugs being overlooked. Fortunately, some violations are actually addressed and resolved by developers. We claim that those violations that are recurrently fixed are likely to be true positives, and an automated approach can learn to repair similar unseen violations. However, there is lack of a systematic way to investigate the distributions on existing violations and fixed ones in the wild, that can provide insights into prioritizing violations for developers, and an effective way to mine code and fix patterns which can help developers easily understand the reasons of leading violations and how to fix them. In this paper, we first collect and track a large number of fixed and unfixed violations across revisions of software. The empirical analyses reveal that there are discrepancies in the distributions of violations that are detected and those that are fixed, in terms of occurrences, spread and categories, which can provide insights into prioritizing violations. To automatically identify patterns in violations and their fixes, we propose an approach that utilizes convolutional neural networks to learn features and clustering to regroup similar instances. We then evaluate the usefulness of the identified fix patterns by applying them to unfixed violations. The results show that developers will accept and merge a majority (69/116) of fixes generated from the inferred fix patterns. It is also noteworthy that the yielded patterns are applicable to four real bugs in the Defects4J major benchmark for software testing and automated repair. [less ▲]

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See detailFeature location benchmark for extractive software product line adoption research using realistic and synthetic Eclipse variants
Martinez, Jabier; Ziadi, Tewfik; Papadakis, Mike UL et al

in Information and Software Technology (2018)

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See detailHow effective are mutation testing tools? An empirical analysis of Java mutation testing tools with manual analysis and real faults
Kintis, Marinos UL; Papadakis, Mike UL; Papadopoulos, Andreas et al

in Empirical Software Engineering (2018)

Mutation analysis is a well-studied, fault-based testing technique. It requires testers to design tests based on a set of artificial defects. The defects help in performing testing activities by measuring ... [more ▼]

Mutation analysis is a well-studied, fault-based testing technique. It requires testers to design tests based on a set of artificial defects. The defects help in performing testing activities by measuring the ratio that is revealed by the candidate tests. Unfortunately, applying mutation to real-world programs requires automated tools due to the vast number of defects involved. In such a case, the effectiveness of the method strongly depends on the peculiarities of the employed tools. Thus, when using automated tools, their implementation inadequacies can lead to inaccurate results. To deal with this issue, we cross-evaluate four mutation testing tools for Java, namely PIT, muJava, Major and the research version of PIT, PITRV, with respect to their fault-detection capabilities. We investigate the strengths of the tools based on: a) a set of real faults and b) manual analysis of the mutants they introduce. We find that there are large differences between the tools’ effectiveness and demonstrate that no tool is able to subsume the others. We also provide results indicating the application cost of the method. Overall, we find that PITRV achieves the best results. In particular, PITRV outperforms the other tools by finding 6% more faults than the other tools combined. [less ▲]

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See detailA training-resistant anomaly detection system
Muller, Steve UL; Lancrenon, Jean; Harpes, Carlo et al

in Computers and Security (2018), 76

Modern network intrusion detection systems rely on machine learning techniques to detect traffic anomalies and thus intruders. However, the ability to learn the network behaviour in real-time comes at a ... [more ▼]

Modern network intrusion detection systems rely on machine learning techniques to detect traffic anomalies and thus intruders. However, the ability to learn the network behaviour in real-time comes at a cost: malicious software can interfere with the learning process, and teach the intrusion detection system to accept dangerous traffic. This paper presents an intrusion detection system (IDS) that is able to detect common network attacks including but not limited to, denial-of-service, bot nets, intrusions, and network scans. With the help of the proposed example IDS, we show to what extent the training attack (and more sophisticated variants of it) has an impact on machine learning based detection schemes, and how it can be detected. © 2018 Elsevier Ltd [less ▲]

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See detailPROFICIENT: Productivity Tool for Semantic Interoperability in an Open IoT Ecosystem
Kolbe, Niklas UL; Robert, Jérémy UL; Kubler, Sylvain et al

in Proceedings of the 14th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (2017, November 07)

The Internet of Things (IoT) is promising to open up opportunities for businesses to offer new services to uncover untapped needs. However, before taking advantage of such opportunities, there are still ... [more ▼]

The Internet of Things (IoT) is promising to open up opportunities for businesses to offer new services to uncover untapped needs. However, before taking advantage of such opportunities, there are still challenges ahead, one of which is the development of strategies to abstract from the heterogeneity of APIs that shape today's IoT. It is becoming increasingly complex for developers and smart connected objects to efficiently discover, parse, aggregate and process data from disparate information systems, as different protocols, data models, and serializations for APIs exist on the market. Standards play an indisputable role in reducing such a complexity, but will not solve all problems related to interoperability. For example, it will remain a permanent need to help and guide data/service providers to efficiently describe the data/services they would like to expose to the IoT. This paper presents PROFICIENT, a productivity tool that fulfills this need, which is showcased and evaluated considering recent open messaging standards and a smart parking scenario. [less ▲]

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See detailOn Locating Malicious Code in Piggybacked Android Apps
Li, Li UL; Li, Daoyuan UL; Bissyande, Tegawendé François D Assise UL et al

in Journal of Computer Science and Technology (2017)

To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to ... [more ▼]

To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy@5 of 83.6% for such packages that are triggered through method invocations and an accuracy@5 of 82.2% for such packages that are triggered independently. [less ▲]

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See detailTowards a Plug-and-Play and Holistic Data Mining Framework for Understanding and Facilitating Operations in Smart Buildings
Li, Daoyuan UL; Bissyande, Tegawendé François D Assise UL; Klein, Jacques UL et al

Report (2017)

Nowadays, a significant portion of the total energy consumption is attributed to the buildings sector. In order to save energy and protect the environment, energy consumption in buildings must be more ... [more ▼]

Nowadays, a significant portion of the total energy consumption is attributed to the buildings sector. In order to save energy and protect the environment, energy consumption in buildings must be more efficient. At the same time, buildings should offer the same (if not more) comfort to their occupants. Consequently, modern buildings have been equipped with various sensors and actuators and interconnected control systems to meet occupants’ requirements. Unfortunately, so far, Building Automation Systems data have not been well-exploited due to technical and cost limitations. Yet, it can be exceptionally beneficial to take full advantage of the data flowing inside buildings in order to diagnose issues, explore solutions and improve occupant-building interactions. This paper presents a plug-and-play and holistic data mining framework named PHoliData for smart buildings to collect, store, visualize and mine useful information and domain knowledge from data in smart buildings. PHoliData allows non technical experts to easily explore and understand their buildings with minimum IT support. An architecture of this framework has been introduced and a prototype has been implemented and tested against real-world settings. Discussions with industry experts have suggested the system to be extremely helpful for understanding buildings, since it can provide hints about energy efficiency improvements. Finally, extensive experiments have demonstrated the feasibility of such a framework in practice and its advantage and potential for buildings operators. [less ▲]

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See detailRaising Time Awareness in Model-Driven Engineering
Benelallam, Amine; Hartmann, Thomas UL; Mouline, Ludovic UL et al

in 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (2017, September)

The conviction that big data analytics is a key for the success of modern businesses is growing deeper, and the mobilisation of companies into adopting it becomes increasingly important. Big data ... [more ▼]

The conviction that big data analytics is a key for the success of modern businesses is growing deeper, and the mobilisation of companies into adopting it becomes increasingly important. Big data integration projects enable companies to capture their relevant data, to efficiently store it, turn it into domain knowledge, and finally monetize it. In this context, historical data, also called temporal data, is becoming increasingly available and delivers means to analyse the history of applications, discover temporal patterns, and predict future trends. Despite the fact that most data that today’s applications are dealing with is inherently temporal current approaches, methodologies, and environments for developing these applications don’t provide sufficient support for handling time. We envision that Model-Driven Engineering (MDE) would be an appropriate ecosystem for a seamless and orthogonal integration of time into domain modelling and processing. In this paper, we investigate the state-of-the-art in MDE techniques and tools in order to identify the missing bricks for raising time-awareness in MDE and outline research directions in this emerging domain. [less ▲]

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See detailKnowledge-based Consistency Index for Fuzzy Pairwise Comparison Matrices
Kubler, Sylvain UL; Derigent, William; Voisin, Alexandre et al

in Knowledge-based Consistency Index for Fuzzy Pairwise Comparison Matrices (2017, July 10)

Abstract—Fuzzy AHP is today one of the most used Multiple Criteria Decision-Making (MCDM) techniques. The main argument to introduce fuzzy set theory within AHP lies in its ability to handle uncertainty ... [more ▼]

Abstract—Fuzzy AHP is today one of the most used Multiple Criteria Decision-Making (MCDM) techniques. The main argument to introduce fuzzy set theory within AHP lies in its ability to handle uncertainty and vagueness arising from decision makers (when performing pairwise comparisons between a set of criteria/alternatives). As humans usually reason with granular information rather than precise one, such pairwise comparisons may contain some degree of inconsistency that needs to be properly tackled to guarantee the relevance of the result/ranking. Over the last decades, several consistency indexes designed for fuzzy pairwise comparison matrices (FPCMs) were proposed, as will be discussed in this article. However, for some decision theory specialists, it appears that most of these indexes fail to be properly “axiomatically” founded, thus leading to misleading results. To overcome this, a new index, referred to as KCI (Knowledge-based Consistency Index) is introduced in this paper, and later compared with an existing index that is axiomatically well founded. The comparison results show that (i) both indexes perform similarly from a consistency measurement perspective, but (ii) KCI contributes to significantly reduce the computation time, which can save expert’s time in some MCDM problems. [less ▲]

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See detailEnriching a Situation Awareness Framework for IoT with Knowledge Base and Reasoning Components
Kolbe, Niklas UL; Zaslavsky, Arkady; Kubler, Sylvain UL et al

in Modeling and Using Context (2017, July)

Theimportanceofsystem-levelcontext-andsituationaware- ness increases with the growth of the Internet of Things (IoT). This paper proposes an integrated approach to situation awareness by providing a ... [more ▼]

Theimportanceofsystem-levelcontext-andsituationaware- ness increases with the growth of the Internet of Things (IoT). This paper proposes an integrated approach to situation awareness by providing a semantically rich situation model together with reliable situation infer- ence based on Context Spaces Theory (CST) and Situation Theory (ST). The paper discusses benefits of integrating the proposed situation aware- ness framework with knowledge base and efficient reasoning techniques taking into account uncertainty and incomplete knowledge about situa- tions. The paper discusses advantages and impact of proposed context adaptation in dynamic IoT environments. Practical issues of two-way mapping between IoT messaging standards and CST are also discussed. [less ▲]

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See detailTowards Semantic Interoperability in an Open IoT Ecosystem for Connected Vehicle Services
Kolbe, Niklas UL; Kubler, Sylvain UL; Robert, Jérémy UL et al

in 2017 IEEE Global Internet of Things Summit (GIoTS) Proceedings (2017, July)

A present challenge in today’s Internet of Things (IoT) ecosystem is to enable interoperability across hetero- geneous systems and service providers. Restricted access to data sources and services limits ... [more ▼]

A present challenge in today’s Internet of Things (IoT) ecosystem is to enable interoperability across hetero- geneous systems and service providers. Restricted access to data sources and services limits the capabilities of a smart city to improve social, environmental and economic aspects. Interoperability in the IoT is concerned with both, messaging interfaces and semantic understanding of heterogeneous data. In this paper, the first building blocks of an emerging open IoT ecosystem developed at the EU level are presented. Se- mantic web technologies are applied to the existing messaging components to support and improve semantic interoperability. The approach is demonstrated with a proof-of-concept for connected vehicle services in a smart city setting. [less ▲]

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See detailImpact of Tool Support in Patch Construction
Koyuncu, Anil UL; Bissyande, Tegawendé François D Assise UL; Kim, Dongsun UL et al

Scientific Conference (2017, July)

In this work, we investigate the practice of patch construction in the Linux kernel development, focusing on the differences between three patching processes: (1) patches crafted entirely manually to fix ... [more ▼]

In this work, we investigate the practice of patch construction in the Linux kernel development, focusing on the differences between three patching processes: (1) patches crafted entirely manually to fix bugs, (2) those that are derived from warnings of bug detection tools, and (3) those that are automatically generated based on fix patterns. With this study, we provide to the research community concrete insights on the practice of patching as well as how the development community is currently embracing research and commercial patching tools to improve productivity in repair. The result of our study shows that tool-supported patches are increasingly adopted by the developer community while manually-written patches are accepted more quickly. Patch application tools enable developers to remain committed to contributing patches to the code base. Our findings also include that, in actual development processes, patches generally implement several change operations spread over the code, even for patches fixing warnings by bug detection tools. Finally, this study has shown that there is an opportunity to directly leverage the output of bug detection tools to readily generate patches that are appropriate for fixing the problem, and that are consistent with manually-written patches. [less ▲]

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See detailAnalyzing Complex Data in Motion at Scale with Temporal Graphs
Hartmann, Thomas UL; Fouquet, François UL; Jimenez, Matthieu UL et al

in Proceedings of the 29th International Conference on Software Engineering and Knowledge Engineering (2017, July)

Modern analytics solutions succeed to understand and predict phenomenons in a large diversity of software systems, from social networks to Internet-of-Things platforms. This success challenges analytics ... [more ▼]

Modern analytics solutions succeed to understand and predict phenomenons in a large diversity of software systems, from social networks to Internet-of-Things platforms. This success challenges analytics algorithms to deal with more and more complex data, which can be structured as graphs and evolve over time. However, the underlying data storage systems that support large-scale data analytics, such as time-series or graph databases, fail to accommodate both dimensions, which limits the integration of more advanced analysis taking into account the history of complex graphs, for example. This paper therefore introduces a formal and practical definition of temporal graphs. Temporal graphs pro- vide a compact representation of time-evolving graphs that can be used to analyze complex data in motion. In particular, we demonstrate with our open-source implementation, named GREYCAT, that the performance of temporal graphs allows analytics solutions to deal with rapidly evolving large-scale graphs. [less ▲]

Detailed reference viewed: 269 (18 UL)