peer reviewed

Industrial reports indicate that flaky tests are one of the primary concerns of software testing mainly due to the false signals they provide. To deal with this issue, researchers have developed tools and techniques aiming at (automatically) identifying flaky tests with encouraging results. However, to reach industrial adoption and practice, these techniques need to be replicated and evaluated extensively on multiple datasets, occasions and settings. In view of this, we perform a replication study of a recently proposed method that predicts flaky tests based on their vocabulary. We thus replicate the original study on three different dimensions. First, we replicate the approach on the same subjects as in the original study but using a different evaluation methodology, i.e., we adopt a time-sensitive selection of training and test sets to better reflect the envisioned use case. Second, we consolidate the findings of the initial study by building a new dataset of 837 flaky tests from 9 projects in a different programming language, i.e., Python while the original study was in Java, which comforts the generalisability of the results. Third, we propose an extension to the original approach by experimenting with different features extracted from the Code Under Test. We find that a more robust validation consistently decreases performance on the reported results of the original study, but, fortunately, the model remains capable to decently predict flaky tests. We find re-assuring results that the vocabulary-based models can also be used to predict test flakiness in Python. Finally, we find that the information lying in the Code Under Test has a limited impact on the performance of the vocabulary-based models.

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.

Ontology search and ranking are key building blocks to establish and reuse shared conceptualizations of domain knowledge on the Web. However, the effectiveness of proposed ontology ranking models is difficult to compare since these are often evaluated on diverse datasets that are limited by their static nature and scale. In this paper, we first introduce the LOVBench dataset as a benchmark for ontology term ranking. With inferred relevance judgments for more than 7000 queries, LOVBench is large enough to perform a comparison study using learning to rank (LTR) with complex ontology ranking models. Instead of relying on relevance judgments from a few experts, we consider implicit feedback from many actual users collected from the Linked Open Vocabularies (LOV) platform. Our approach further enables continuous updates of the benchmark, capturing the evolution of ontologies’ relevance in an ever-changing data community. Second, we compare the performance of several feature configurations from the literature using LOVBench in LTR settings and discuss the results in the context of the observed real-world user behavior. Our experimental results show that feature configurations which are (i) well-suited to the user behavior, (ii) cover all features types, and (iii) consider decomposition of features can significantly improve the ranking performance.

peer reviewed

Android malware is now pervasive and evolving rapidly. Thousands of malware samples are discovered every day with new models of attacks. The growth of these threats has come hand in hand with the proliferation of collective repositories sharing the latest specimens. Having access to a large number of samples opens new research directions aiming at efficiently vetting apps. However, automatically inferring a reference ground-truth from those repositories is not straightforward and can inadvertently lead to unforeseen misconceptions. On the one hand, samples are often mis-labeled as different parties use distinct naming schemes for the same sample. On the other hand, samples are frequently mis-classified due to conceptual errors made during labeling processes. In this paper, we analyze the associations between all labels given by different vendors and we propose a system called EUPHONY to systematically unify common samples into family groups. The key novelty of our approach is that no a-priori knowledge on malware families is needed. We evaluate our approach using reference datasets and more than 0.4 million additional samples outside of these datasets. Results show that EUPHONY provides competitive performance against the state-of-the-art.

We present a growing collection of Android Applications collected from several sources, including the official Google Play app market. Our dataset, AndroZoo, currently contains more than three million apps, each of which has been analysed by tens of different Antivirus products to know which applications are detected as Malware. We provide this dataset to contribute to ongoing research efforts, as well as to enable new potential research topics on Android Apps. By releasing our dataset to the research community, we also aim at encouraging our fellow researchers to engage in reproducible experiments.

MapReduce query processing systems translate a query statement into a query plan, consisting of a set of MapReduce jobs to be executed in distributed machines. During query translation, these query systems uniformly allocate computing resources to each job by delegating the same tuning to the entire query plan. However, jobs may implement their own collection of operators, which lead to different usage of computing resources. In this paper we propose an adaptive tuning mechanism that enables setting specific resources to each job within a query plan. Our adaptive mechanism relies on a data structure that maps jobs to tuning codes by analyzing source code and log files. This adaptive mechanism allows delegating specific resources to the query plan at runtime as the data structure hosts specific pre-computed tuning codes.

Over the last decade, large amounts of concurrent transactions have been generated from different sources, such as, Internet-based systems, mobile applications, smart-homes and cars. High-throughput transaction processing is becoming commonplace, however there is no testing technique for validating non functional aspects of DBMS under transaction flooding workloads. In this paper we propose a database state machine to represent the states of DBMS when processing concurrent transactions. The state transitions are forced by increasing concurrency of the testing workload. Preliminary results show the effectiveness of our approach to drive the system among different performance states and to find related defects.