1,720,969 research outputs found
Specifying and Fuzzing Machine-Learning Models
Get PDFMachine Learning (ML) models are increasingly prevalent in safety critical systems, from self-driving cars to aviation, where failures can result in catastrophic outcomes. While researchers have addressed specific properties like robustness and fairness, specifying and checking general functional-correctness expectations from ML models remains challenging. This thesis introduces novel tools and approaches inspired by software testing concepts to specify and fuzz ML artifacts for their functional correctness. Software testing identifies bugs by running programs with given inputs, facing two main challenges: generating test inputs and finding test oracles. Fuzzing is a widely adopted method for generating test inputs, while specifications address the oracle problem. These techniques and concepts have proven effective and crucial for validating software reliability. We tailor these methods to assess ML model reliability. One of the biggest recent advancements in machine learning has been in solving sequential decision-making problems where agents learn action policies. In this thesis, we devise techniques to test action policies’ reliability. Beyond checking if policies lead to undesirable outcomes, we address: how can we identify undesirable yet avoidable outcomes? We present novel test oracles based on metamorphic relations and develop the fuzz framework to identify bugs in action policies. Next, we formalize metamorphic relations as k safety properties, or hyperproperties, describing relationships between multiple input-output pairs. We show hyperproperties can specify functional correctness across various ML domains. To express these, we create NOMOS, a declarative, domain-agnostic specification language with an automated testing framework. We demonstrate its effectiveness in finding bugs across various domains including image classification, sentiment analysis, and speech recognition. We also extend NOMOS to accommodate code translation models. Overall, this thesis contributes to the field by providing a specification language and novel automated testing frameworks to validate the reliability and safety of ML models which are now prevalent in our daily lives
Automatically Testing Functional Properties of Code Translation Models
Get PDFLarge language models are becoming increasingly practical for translating
code across programming languages, a process known as . Even
though automated transpilation significantly boosts developer productivity, a
key concern is whether the generated code is correct. Existing work initially
used manually crafted test suites to test the translations of a small corpus of
programs; these test suites were later automated. In contrast, we devise the
first approach for automated, functional, property-based testing of code
translation models. Our general, user-provided specifications about the
transpiled code capture a range of properties, from purely syntactic to purely
semantic ones. As shown by our experiments, this approach is very effective in
detecting property violations in popular code translation models, and
therefore, in evaluating model quality with respect to given properties. We
also go a step further and explore the usage scenario where a user simply aims
to obtain a correct translation of some code with respect to certain properties
without necessarily being concerned about the overall quality of the model. To
this purpose, we develop the first property-guided search procedure for code
translation models, where a model is repeatedly queried with slightly different
parameters to produce alternative and potentially more correct translations.
Our results show that this search procedure helps to obtain significantly
better code translations.Comment: 13 pages including appendix and reference
Importance-Driven Deep Learning System Testing
Get PDFDeep Learning (DL) systems are key enablers for engineering intelligent applications due to their ability to solve complex tasks such as image recognition and machine translation. Nevertheless, using DL systems in safety- and security-critical applications requires to provide testing evidence for their dependable operation. Recent research in this direction focuses on adapting testing criteria from traditional software engineering as a means of increasing confidence for their correct behaviour. However, they are inadequate in capturing the intrinsic properties exhibited by these systems. We bridge this gap by introducing DeepImportance, a systematic testing methodology accompanied by an Importance-Driven (IDC) test adequacy criterion for DL systems. Applying IDC enables to establish a layer-wise functional understanding of the importance of DL system components and use this information to guide the generation of semantically-diverse test sets. Our empirical evaluation on several DL systems, across multiple DL datasets and with state-of-the-art adversarial generation techniques demonstrates the usefulness and effectiveness of DeepImportance and its ability to guide the engineering of more robust DL systems
Automatically Testing Functional Properties of Code Translation Models
No full textLarge language models are becoming increasingly practical for translating code across programming languages, a process known as transpiling. Even though automated transpilation significantly boosts developer productivity, a key concern is whether the generated code is correct. Existing work initially used manually crafted test suites to test the translations of a small corpus of programs; these test suites were later automated. In contrast, we devise the first approach for automated, functional, property-based testing of code translation models. Our general, user-provided specifications about the transpiled code capture a range of properties, from purely syntactic to purely semantic ones. As shown by our experiments, this approach is very effective in detecting property violations in popular code translation models, and therefore, in evaluating model quality with respect to given properties. We also go a step further and explore the usage scenario where a user simply aims to obtain a correct translation of some code with respect to certain properties without necessarily being concerned about the overall quality of the model. To this purpose, we develop the first property-guided search procedure for code translation models, where a model is repeatedly queried with slightly different parameters to produce alternative and potentially more correct translations. Our results show that this search procedure helps to obtain significantly better code translations
Automatically Testing Functional Properties of Code Translation Models
No full textLarge language models are becoming increasingly practical for translating code across programming languages, a process known as transpiling. Even though automated transpilation significantly boosts developer productivity, a key concern is whether the generated code is correct. Existing work initially used manually crafted test suites to test the translations of a small corpus of programs; these test suites were later automated. In contrast, we devise the first approach for automated, functional, property-based testing of code translation models. Our general, user-provided specifications about the transpiled code capture a range of properties, from purely syntactic to purely semantic ones. As shown by our experiments, this approach is very effective in detecting property violations in popular code translation models, and therefore, in evaluating model quality with respect to given properties. We also go a step further and explore the usage scenario where a user simply aims to obtain a correct translation of some code with respect to certain properties without necessarily being concerned about the overall quality of the model. To this purpose, we develop the first property-guided search procedure for code translation models, where a model is repeatedly queried with slightly different parameters to produce alternative and potentially more correct translations. Our results show that this search procedure helps to obtain significantly better code translations
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Automatically Testing Functional Properties of Code Translation Models
Get PDFLarge language models are becoming increasingly practical for translating code across programming languages, a process known as transpiling. Even though automated transpilation significantly boosts developer productivity, a key concern is whether the generated code is correct. Existing work initially used manually crafted test suites to test the translations of a small corpus of programs; these test suites were later automated. In contrast, we devise the first approach for automated, functional, property-based testing of code translation models. Our general, user-provided specifications about the transpiled code capture a range of properties, from purely syntactic to purely semantic ones. As shown by our experiments, this approach is very effective in detecting property violations in popular code translation models, and therefore, in evaluating model quality with respect to given properties. We also go a step further and explore the usage scenario where a user simply aims to obtain a correct translation of some code with respect to certain properties without necessarily being concerned about the overall quality of the model. To this purpose, we develop the first property-guided search procedure for code translation models, where a model is repeatedly queried with slightly different parameters to produce alternative and potentially more correct translations. Our results show that this search procedure helps to obtain significantly better code translations
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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