1,631 research outputs found
Generating valid test data through data cloning
One of the most difficult, time-consuming and error-prone tasks during software testing is that of manually generating the data required to properly run the test. This is even harder when we need to generate data of a certain size and such that it satisfies a set of conditions, or business rules, specified over an ontology. To solve this problem, some proposals exist to automatically generate database sample data. However, they are only able to generate data satisfying primary or foreign key constraints but not more complex business rules in the ontology. We propose here a more general solution for generating test data which is able to deal with expressive business rules. Our approach, which is entirely based on the chase algorithm, first generates a small sample of valid test data (by means of an automated reasoner), then clones this sample data, and finally, relates the cloned data with the original data. All the steps are performed iteratively until a valid database of a certain size is obtained. We theoretically prove the correctness of our approach, and experimentally show its practical applicability.This work is partially supported by the SUDOQU project, PID2021-126436OB-C21 from MCIN/AEI, 10.13039/501100011033, FEDER, UE and by the Generalitat de Catalunya, Spain (under 2017-SGR-1749); Sergi Nadal is partly supported by the Spanish Ministerio de Ciencia e Innovación , as well as the European Union - NextGenerationEU, under project FJC2020-045809-I.Peer ReviewedPostprint (published version
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Evaluating Architectural Safeguards for Uncertain AI Black-Box Components
Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability
Consistent Query Answering for Primary Keys on Rooted Tree Queries
We study the data complexity of consistent query answering (CQA) on databases
that may violate the primary key constraints. A repair is a maximal subset of
the database satisfying the primary key constraints. For a Boolean query q, the
problem CERTAINTY(q) takes a database as input, and asks whether or not each
repair satisfies q. The computational complexity of CERTAINTY(q) has been
established whenever q is a self-join-free Boolean conjunctive query, or a (not
necessarily self-join-free) Boolean path query. In this paper, we take one more
step towards a general classification for all Boolean conjunctive queries by
considering the class of rooted tree queries. In particular, we show that for
every rooted tree query q, CERTAINTY(q) is in FO, NL-hard LFP, or
coNP-complete, and it is decidable (in polynomial time), given q, which of the
three cases applies. We also extend our classification to larger classes of
queries with simple primary keys. Our classification criteria rely on query
homomorphisms and our polynomial-time fixpoint algorithm is based on a novel
use of context-free grammar (CFG).Comment: To appear in PODS'2
Automated Testing of Software Upgrades for Android Systems
Apps’ pervasive role in our society motivates researchers to develop automated techniques ensuring dependability through testing. However, although App updates are frequent and software engineers would like to prioritize the testing of updated features, automated testing techniques verify entire Apps and thus waste resources. Further, most testing techniques can detect only crashing failures, necessitating visual inspection of outputs to detect functional failures, which is a costly task. Despite efforts to automatically derive oracles for functional failures, the effectiveness of existing approaches is limited. Therefore, instead of automating human tasks, it seems preferable to minimize what should be visually inspected by engineers.
To address the problems above, in this dissertation, we propose approaches to maximize testing effectiveness while containing test execution time and human effort.
First, we present ATUA (Automated Testing of Updates for Apps), a model-based approach that synthesizes App models with static analysis, integrates a dynamically refined state abstraction function, and combines complementary testing strategies, thus enabling ATUA to generate a small set of inputs that exercise only the code affected by updates. A large empirical evaluation conducted with 72 App versions belonging to nine popular Android Apps has shown that ATUA is more effective and less effort-intensive than state-of-the-art approaches when testing App updates.
Second, we present CALM (Continuous Adaptation of Learned Models), an automated App testing approach that efficiently tests App updates by adapting App models learned when automatically testing previous App versions. CALM minimizes the number of App screens to be visualized by software testers while maximizing the percentage of updated methods and instructions exercised. Our empirical evaluation shows that CALM exercises a significantly higher proportion of updated methods and instructions than baselines for the same maximum number of App screens to be visually inspected. Further, in common update scenarios, where only a small fraction of methods are updated, CALM is even quicker to outperform all competing approaches more significantly.
Finally, we minimize test oracle cost by defining strategies for selecting, for visual inspection, a subset of the App outputs. We assessed 26 strategies, relying on either code coverage or action effect, on Apps affected by functional faults confirmed by their developers. Our empirical evaluation has shown that our strategies have the potential to enable the identification of a large proportion of faults. By combining code coverage with action effect, it is possible to reduce oracle cost by about 41.2% while enabling engineers to detect all the faults exercised by test automation approaches
Explainable temporal data mining techniques to support the prediction task in Medicine
In the last decades, the increasing amount of data available in all fields raises the necessity to discover new knowledge and explain the hidden information found. On one hand, the rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, results to users. In the biomedical informatics and computer science communities, there is considerable discussion about the `` un-explainable" nature of artificial intelligence, where often algorithms and systems leave users, and even developers, in the dark with respect to how results were obtained. Especially in the biomedical context, the necessity to explain an artificial intelligence system result is legitimate of the importance of patient safety. On the other hand, current database systems enable us to store huge quantities of data. Their analysis through data mining techniques provides the possibility to extract relevant knowledge and useful hidden information. Relationships and patterns within these data could provide new medical knowledge. The analysis of such healthcare/medical data collections could greatly help to observe the health conditions of the population and extract useful information that can be exploited in the assessment of healthcare/medical processes. Particularly, the prediction of medical events is essential for preventing disease, understanding disease mechanisms, and increasing patient quality of care. In this context, an important aspect is to verify whether the database content supports the capability of predicting future events. In this thesis, we start addressing the problem of explainability, discussing some of the most significant challenges need to be addressed with scientific and engineering rigor in a variety of biomedical domains. We analyze the ``temporal component" of explainability, focusing on detailing different perspectives such as: the use of temporal data, the temporal task, the temporal reasoning, and the dynamics of explainability in respect to the user perspective and to knowledge. Starting from this panorama, we focus our attention on two different temporal data mining techniques. The first one, based on trend abstractions, starting from the concept of Trend-Event Pattern and moving through the concept of prediction, we propose a new kind of predictive temporal patterns, namely Predictive Trend-Event Patterns (PTE-Ps). The framework aims to combine complex temporal features to extract a compact and non-redundant predictive set of patterns composed by such temporal features. The second one, based on functional dependencies, we propose a methodology for deriving a new kind of approximate temporal functional dependencies, called Approximate Predictive Functional Dependencies (APFDs), based on a three-window framework. We then discuss the concept of approximation, the data complexity of deriving an APFD, the introduction of two new error measures, and finally the quality of APFDs in terms of coverage and reliability. Exploiting these methodologies, we analyze intensive care unit data from the MIMIC dataset
Chatbots for Modelling, Modelling of Chatbots
Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202
The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verification
This paper presents the FormAI dataset, a large collection of 112, 000
AI-generated compilable and independent C programs with vulnerability
classification. We introduce a dynamic zero-shot prompting technique
constructed to spawn diverse programs utilizing Large Language Models (LLMs).
The dataset is generated by GPT-3.5-turbo and comprises programs with varying
levels of complexity. Some programs handle complicated tasks like network
management, table games, or encryption, while others deal with simpler tasks
like string manipulation. Every program is labeled with the vulnerabilities
found within the source code, indicating the type, line number, and vulnerable
function name. This is accomplished by employing a formal verification method
using the Efficient SMT-based Bounded Model Checker (ESBMC), which uses model
checking, abstract interpretation, constraint programming, and satisfiability
modulo theories to reason over safety/security properties in programs. This
approach definitively detects vulnerabilities and offers a formal model known
as a counterexample, thus eliminating the possibility of generating false
positive reports. We have associated the identified vulnerabilities with Common
Weakness Enumeration (CWE) numbers. We make the source code available for the
112, 000 programs, accompanied by a separate file containing the
vulnerabilities detected in each program, making the dataset ideal for training
LLMs and machine learning algorithms. Our study unveiled that according to
ESBMC, 51.24% of the programs generated by GPT-3.5 contained vulnerabilities,
thereby presenting considerable risks to software safety and security.Comment: https://github.com/FormAI-Datase
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