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Many modern software systems are built as a set of autonomous software components (also called agents) that collaborate with each other and are situated in an environment. To keep these multiagent systems operational under abnormal circumstances, it is crucial to make them resilient. Existing solutions are often centralised and rely on information manually provided by experts at design time, making such solutions rigid and limiting the autonomy and adaptability of the system. In this work, we propose a cooperative strategy focused on the identification of the root causes of quality requirement violations in multiagent systems. This strategy allows agents to cooperate with each other in order to identify whether these violations come from service providers, associated components, or the communication infrastructure. From this identification process, agents are able to adapt their behaviour in order to mitigate and solve existing abnormalities with the aim of normalising system operation. This strategy consists of an interaction protocol that, together with the proposed algorithms, allow agents playing the protocol roles to diagnose problems to be repaired. We evaluate our proposal with the implementation of a service-oriented system. The results demonstrate that our solution enables the correct identification of different sources of failures, favouring the selection of the most suitable actions to be taken to overcome abnormal situations.

In this study, we propose the early adoption of Explainable AI (XAI) with a focus on three properties: Quality of explanation, the explanation summaries should be consistent across multiple XAI methods; Architectural Compatibility, for effective integration in XAI, the architecture styles of both the XAI methods and the models to be explained must be compatible with the framework; Configurable operations, XAI explanations are operable, akin to machine learning operations. Thus, an explanation for AI models should be reproducible and tractable to be trustworthy. We present XAIport, a framework of XAI microservices encapsulated into Open APIs to deliver early explanations as observation for learning model quality assurance. XAIport enables configurable XAI operations along with machine learning development. We quantify the operational costs of incorporating XAI with three cloud computer vision services on Microsoft Azure Cognitive Services, Google Cloud Vertex AI, and Amazon Rekognition. Our findings show comparable operational costs between XAI and traditional machine learning, with XAIport significantly improving both cloud AI model performance and explanation stability., Comment: Accepted at the ICSE'24 conference, NIER track

Logging plays a crucial role in software engineering because it is key to perform various tasks including debugging, performance analysis, and detection of anomalies. Despite the importance of log data, the practice of logging still suffers from the lack of common guidelines and best practices. Recent studies investigated logging in C/C++ and Java open-source systems. In this paper, we complement these studies by conducting the first empirical study on logging practices in the Linux kernel, one of the most elaborate open-source development projects in the computer industry. We analyze 22 Linux releases with a focus on three main aspects: the pervasiveness of logging in Linux, the types of changes made to logging statements, and the rationale behind these changes. Our findings show that logging code accounts for 3.73% of the total source code in the Linux kernel, distributed across 72.36% of Linux files. We also found that the distribution of logging statements across Linux subsystems and their components vary significantly with no apparent reasons, suggesting that developers use different criteria when logging. In addition, we observed a slow decrease in the use of logging-reduction of 9.27% between versions v4.3 and v5.3. The majority of changes in logging code are made to fix language issues, modify log levels, and upgrade logging code to use new logging libraries, with the overall goal of improving the precision and consistency of the log output. Many recommendations are derived from our findings such as the use of static analysis tools to detect log-related issues, the adoption of common writing styles to improve the quality of log messages, the development of conventions to guide developers when selecting log levels, the establishment of review sessions to review logging code, and so on. [...], Comment: Accepted for publication in the Empirical Software Engineering journal

Logs provide users with useful insights to help with a variety of development and operations tasks. The problem is that logs are often unstructured, making their analysis a complex task. This is mainly due to the lack of guidelines and best practices for logging, combined with a large number of logging libraries at the disposal of software developers. There exist studies that aim to parse automatically large logs. The main objective is to extract templates from samples of log data that are used to recognize future logs. In this paper, we propose AWSOM-LP, a powerful log parsing and abstraction tool, which is highly accurate, stable, and efficient. AWSOM-LP is built on the idea of applying pattern recognition and frequency analysis. First, log events are organized into patterns using a simple text processing method. Frequency analysis is then applied locally to instances of the same group to identify static and dynamic content of log events. When applied to 16 log datasets of the the LogPai project, AWSOM-LP achieves an average grouping accuracy of 93.5%, which outperforms the accuracy of five leading log parsing tools namely, Logram, Lenma, Drain, IPLoM and AEL. Additionally, AWSOM-LP can generate more than 80% of the final log templates from 10% to 50% of the entire log dataset and can parse up to a million log events in an average time of 5 minutes. AWSOM-LP is available online as an open source. It can be used by practitioners and researchers to parse effectively and efficiently large log files so as to support log analysis tasks., Comment: Article submitted to IEEE Transactions on Software Engineering (TSE), 13 pages

Bug reports (BR) contain vital information that can help triaging teams prioritize and assign bugs to developers who will provide the fixes. However, studies have shown that BR fields often contain incorrect information that need to be reassigned, which delays the bug fixing process. There exist approaches for predicting whether a BR field should be reassigned or not. These studies use mainly BR descriptions and traditional machine learning algorithms (SVM, KNN, etc.). As such, they do not fully benefit from the sequential order of information in BR data, such as function call sequences in BR stack traces, which may be valuable for improving the prediction accuracy. In this paper, we propose a novel approach, called EnHMM, for predicting the reassignment of BR fields using ensemble Hidden Markov Models (HMMs), trained on stack traces. EnHMM leverages the natural ability of HMMs to represent sequential data to model the temporal order of function calls in BR stack traces. When applied to Eclipse and Gnome BR repositories, EnHMM achieves an average precision, recall, and F-measure of 54%, 76%, and 60% on Eclipse dataset and 41%, 69%, and 51% on Gnome dataset. We also found that EnHMM improves over the best single HMM by 36% for Eclipse and 76% for Gnome. Finally, when comparing EnHMM to Im.ML.KNN, a recent approach in the field, we found that the average F-measure score of EnHMM improves the average F-measure of Im.ML.KNN by 6.80% and improves the average recall of Im.ML.KNN by 36.09%. However, the average precision of EnHMM is lower than that of Im.ML.KNN (53.93% as opposed to 56.71%)., Comment: Published in Proceedings of the 28th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2021), 11 pages, 7 figures

This paper addresses the challenge of understanding the waiting dependencies between the threads and hardware resources required to complete a task. The objective is to improve software performance by detecting the underlying bottlenecks caused by system-level blocking dependencies. In this paper, we use a system level tracing approach to extract a Waiting Dependency Graph that shows the breakdown of a task execution among all the interleaving threads and resources. The method allows developers and system administrators to quickly discover how the total execution time is divided among its interacting threads and resources. Ultimately, the method helps detecting bottlenecks and highlighting their possible causes. Our experiments show the effectiveness of the proposed approach in several industry-level use cases. Three performance anomalies are analysed and explained using the proposed approach. Evaluating the method efficiency reveals that the imposed overhead never exceeds 10.1%, therefore making it suitable for in-production environments., Comment: 11 pages, 8 figures, IEEE SCAM 2020

In this paper we discuss existing approaches for Bitcoin payments, as suitable for a small business for small-value transactions. We develop an evaluation framework utilizing security, usability, deployability criteria,, examine several existing systems, tools. Following a requirements engineering approach, we designed, implemented a new Point of Sale (PoS) system that satisfies an optimal set of criteria within our evaluation framework. Our open source system, Aunja PoS, has been deployed in a real world cafe since October 2014., Comment: Advanced and Trusted Computing 2016 Intl IEEE Conferences, 8 pages