TraceSim: An alignment method for computing stack trace similarity

ABSTRACT: Software systems can automatically submit crash reports to a repository for investigation when program failures occur. A significant portion of these crash reports are duplicate, i.e., they are caused by the same software issue. Therefore, if the volume of submitted reports is very large, automatic grouping of duplicate crash reports can significantly ease and speed up analysis of software failures. This task is known as crash report deduplication. Given a huge volume of incoming reports, increasing quality of deduplication is an important task. The majority of studies address it via information retrieval or sequence matching methods based on the similarity of stack traces from two crash reports. While information retrieval methods disregard the position of a frame in a stack trace, the existing works based on sequence matching algorithms do not fully consider subroutine global frequency and unmatched frames. Besides, due to data distribution differences among software projects, parameters that are learned using machine learning algorithms are necessary to provide more flexibility to the methods. In this paper, we propose TraceSim – an approach for crash report deduplication which combines TF-IDF, optimum global alignment, and machine learning (ML) in a novel way. Moreover, we propose a new evaluation methodology for this task that is more comprehensive and robust than previously used evaluation approaches. TraceSim significantly outperforms seven baselines and state-of-the-art methods in the majority of the scenarios. It is the only approach that achieves competitive results on all datasets regarding all considered metrics. Moreover, we conduct an extensive ablation study that demonstrates the importance of each TraceSim’s element to its final performance and robustness. Finally, we provide the source code for all considered methods and evaluation methodology as well as the created datasets

The role of ATP and adenosine in the brain under normoxic and ischemic conditions

By taking advantage of some recently synthesized compounds that are able to block ecto-ATPase activity, we demonstrated that adenosine triphosphate (ATP) in the hippocampus exerts an inhibitory action independent of its degradation to adenosine. In addition, tonic activation of P2 receptors contributes to the normally recorded excitatory neurotransmission. The role of P2 receptors becomes critical during ischemia when extracellular ATP concentrations increase. Under such conditions, P2 antagonism is protective. Although ATP exerts a detrimental role under ischemia, it also exerts a trophic role in terms of cell division and differentiation. We recently reported that ATP is spontaneously released from human mesenchymal stem cells (hMSCs) in culture. Moreover, it decreases hMSC proliferation rate at early stages of culture. Increased hMSC differentiation could account for an ATP-induced decrease in cell proliferation. ATP as a homeostatic regulator might exert a different effect on cell trophism according to the rate of its efflux and receptor expression during the cell life cycle. During ischemia, adenosine formed by intracellular ATP escapes from cells through the equilibrative transporter. The protective role of adenosine A1 receptors during ischemia is well accepted. However, the use of selective A1 agonists is hampered by unwanted peripheral effects, thus attention has been focused on A2A and A3 receptors. The protective effects of A2A antagonists in brain ischemia may be largely due to reduced glutamate outflow from neurones and glial cells. Reduced activation of p38 mitogen-activated protein kinases that are involved in neuronal death through transcriptional mechanisms may also contribute to protection by A2A antagonism. Evidence that A3 receptor antagonism may be protective after ischemia is also reported

Algorithms and Learning Models for Bug Report Deduplication

RÉSUMÉ: Dans les projets logiciels, une pratique courante consiste à utiliser des système de suivi des bugs (BTSs) afin de gérer et suivre les enregistrements de bogues. Une tâche cruciale pour les BTS consiste à identifier si un nouveau rapport décrit un bogue qui a déjà été signalé, c’est-à-dire s’il s’agit d’un rapport double. La déduplication est également particulièrement pertinente pour les projets dans lesquels les applications sont équipées de systèmes automatisés de signalement des plantages. Ces systèmes sont capables de collecter automatiquement les informations sur un platage et ils regroupent ces informations dans un document, appelé rapport de plantage, qui est soumis dans des les référentiels des plantages. Une partie importante des rapports soumis est en double et, par conséquent, leur détection est importante pour un processus de maintenance logicielle efficace. En raison du volume considérable de soumissions, en particulier dans les applications avec une large base d’utilisateurs, la déduplication manuelle des nouveaux rapports dans les BTS et dans les les référentiels de plantages peut être longue et laborieuse. Par conséquent, en pratique, une telle tâche nécessite le soutien de méthodes automatiques.----------ABSTRACT: In software projects, a popular practice is to employ Bug Tracking Systems (BTSs) to manage and track records of bugs. A crucial task for BTSs consists in identifying whether a new report describes a bug that was previously reported or not, i.e., if it is a duplicate report. Deduplication is also particularly relevant for projects where applications are equipped with automated crash reporting systems. These systems are able to automatically collect information about a crash, then grouping it in a so-called crash report. Given the current industrial practice, repositories of crash reports contain a significant amount of duplicate crash reports and, thus, their detection is important for an effective software maintenance process. Due to the considerable submission volume, specially in applications with a wide user base, the manual deduplication of new reports in both BTSs and crash repositories can be time-consuming and laborious. Hence, in practice, such task requires the support of automatic methods

S3M: Siamese Stack (Trace) Similarity Measure

Automatic crash reporting systems have become a de-facto standard in software development. These systems monitor target software, and if a crash occurs they send details to a backend application. Later on, these reports are aggregated and used in the development process to 1) understand whether it is a new or an existing issue, 2) assign these bugs to appropriate developers, and 3) gain a general overview of the application's bug landscape. The efficiency of report aggregation and subsequent operations heavily depends on the quality of the report similarity metric. However, a distinctive feature of this kind of report is that no textual input from the user (i.e., bug description) is available: it contains only stack trace information. In this paper, we present S3M ("extreme") -- the first approach to computing stack trace similarity based on deep learning. It is based on a siamese architecture that uses a biLSTM encoder and a fully-connected classifier to compute similarity. Our experiments demonstrate the superiority of our approach over the state-of-the-art on both open-sourced data and a private JetBrains dataset. Additionally, we review the impact of stack trace trimming on the quality of the results

Role of cannabinoids in alcohol-induced neuroinflammation

Alcohol is a psychoactive substance highly used worldwide, whose harmful use might cause a broad range of mental and behavioural disorders. Underlying brain impact, the neuroinflammatory response induced by alcohol is recognised as a key contributing factor in the progression of other neuropathological processes, such as neurodegeneration. These sequels are determined by multiple factors, including age of exposure. Strikingly, it seems that the endocannabinoid system modulation could regulate the alcohol-induced neuroinflammation. Although direct CB1 activation can worsen alcohol consequences, targeting other components of the expanded endocannabinoid system may counterbalance the pro-inflammatory response. Indeed, specific modulations of the expanded endocannabinoid system have been proved to exert anti-inflammatory effects, primarily through the CB2 and PPARγ signalling. Among them, some endo- and exogeneous cannabinoids can block certain pro-inflammatory mediators, such as NF-κB, thereby neutralizing the neuroinflammatory intracellular cascades. Furthermore, a number of cannabinoids are able to activate complementary anti-inflammatory pathways, which are necessary for the transition from chronically overactivated microglia to a regenerative microglial phenotype. Thus, cannabinoid modulation provides cooperative anti-inflammatory mechanisms that may be advantageous to resolve a pathological neuroinflammation in an alcohol-dependent context.This study was funded by Ministerio de Economia y Competitividad (grant number SAF2016-75966-R-FEDER), Ministerio de Sanidad (Retic-ISCIII, RD16/017/010 and Plan Nacional sobre Drogas2018/007). This study was funded by FI-AGAUR (grant number 2019FI_B 0008) and by Ministerio de Economía and Competitividad (FPI, grant number BES-2017-080066). The Department of Experimental and Health Sciences (UPF) is an “Unidad de Excelencia María de Maeztu” funded by the MINECO (Ref. MDM-2014-0370)

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present