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Today’s software development is typically driven by incremental changes made to software to implement a new functionality, fix a bug, or improve its performance and security. Each change request is often described as an issue. Recent studies suggest that a set of components (e.g., software modules) relevant to the resolution of an issue is one of the most important information provided with the issue that software engineers often rely on. However, assigning an issue to the correct component(s) is challenging, especially for large-scale projects which have up to hundreds of components. In this paper, we propose a predictive model which learns from historical issue reports and recommends the most relevant components for new issues. Our model uses Long Short-Term Memory, a deep learning technique, to automatically learn semantic features representing an issue report, and combines them with the traditional textual similarity features. An extensive evaluation on 142,025 issues from 11 large projects shows that our approach outperforms one common baseline, two state-of-the-art techniques, and six alternative techniques with an improvement of 16.70%–66.31% on average across all projects in predictive performance.

There is a global consensus on the need to reduce our collective carbon footprint. While much research attention has focused on developing alternative energy sources, automotive technologies or waste disposal techniques, we often ignore the fact that the ability to optimize (existing) operations to reduce their emissions impact is fundamental to this exercise. Business process management (BPM) technology, with its focus on understanding, modelling and improving/optimizing business processes, is a key starting point. Process modelling technology has applications beyond what we would traditionally describe as business processes - we can also model and improve manufacturing and other \"physical\" processes. This paper describes the contours of the emerging research landscape in green business process management and presents some early results in this area.

The recent advent of data protection laws and regulations has emerged to protect privacy and personal information of individuals. As the cases of privacy breaches and vulnerabilities are rapidly increasing, people are aware and more concerned about their privacy. These bring a significant attention to software development teams to address privacy concerns in developing software applications. As today’s software development adopts an agile, issue-driven approach, issues in an issue tracking system become a centralised pool that gathers new requirements, requests for modification and all the tasks of the software project. Hence, establishing an alignment between those issues and privacy requirements is an important step in developing privacy-aware software systems. This alignment also facilitates privacy compliance checking which may be required as an underlying part of regulations for organisations. However, manually establishing those alignments is labour intensive and time consuming. In this paper, we explore a wide range of machine learning and natural language processing techniques which can automatically classify privacy requirements in issue reports. We employ six popular techniques namely Bag-of-Words (BoW), N-gram Inverse Document Frequency (N-gram IDF), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Convolutional Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) to perform the classification on privacy-related issue reports in Google Chrome and Moodle projects. The evaluation showed that BoW, N-gram IDF, TF-IDF and Word2Vec techniques are suitable for classifying privacy requirements in those issue reports. In addition, N-gram IDF is the best performer in both projects.

We model a multiagent system (MAS) in socio-technical terms, combining a social layer consisting of norms with a technical layer consisting of actions that the agents execute. This approach emphasizes autonomy, and makes assumptions about both the social and technical layers explicit. Autonomy means that agents may violate norms. In our approach, agents are computational entities, with each representing a different stakeholder. We express stakeholder requirements of the form that a MAS is resilient in that it can recover (sufficiently) from a failure within a (sufficiently short) duration. We present ReNo , a framework that computes probabilistic and temporal guarantees on whether the underlying requirements are met or, if failed, recovered. ReNo supports the refinement of the specification of a socio-technical system through methodological guidelines to meet the stated requirements. An important contribution of ReNo is that it shows how the social and technical layers can be modeled jointly to enable the construction of resilient systems of autonomous agents. We demonstrate ReNo using a manufacturing scenario with competing public, industrial, and environmental requirements.

Automated Program Repair has attracted significant research in recent years, leading to diverse techniques which focus on two main directions: search-based and semantic-based program repair. The former techniques often face challenges due to the vast search space, resulting in difficulties in identifying correct solutions, while the latter approaches are constrained by the capabilities of the underlying semantic analyser, limiting their scalability. In this paper, we propose NEVERMORE, a novel learning-based mechanism inspired by the adversarial nature of bugs and fixes. NEVERMORE is built upon the Generative Adversarial Networks architecture and trained on historical bug fixes to generate repairs that closely mimic human-produced fixes. Our empirical evaluation on 500 real-world bugs demonstrates the effectiveness of NEVERMORE in bug-fixing, generating repairs that match human fixes for 21.2% of the examined bugs. Moreover, we evaluate NEVERMORE on the Defects4J dataset, where our approach generates repairs for 4 bugs that remained unresolved by state-of-the-art baselines. NEVERMORE also fixes another 8 bugs which were only resolved by a subset of these baselines. Finally, we conduct an in-depth analysis of the impact of input and training styles on NEVERMORE’s performance, revealing where the chosen style influences the model’s bug-fixing capabilities.

Technical Debt occurs when development teams favour short-term operability over long-term stability. Since this places software maintainability at risk, technical debt requires early attention to avoid paying for accumulated interest. Most of the existing work focuses on detecting technical debt using code comments, known as Self-Admitted Technical Debt (SATD). However, there are many cases where technical debt instances are not explicitly acknowledged but deeply hidden in the code. In this paper, we propose a framework that caters for the absence of SATD comments in code. Our Self-Admitted Technical Debt Identification and Description (SATDID) framework determines if technical debt should be self-admitted for an input code fragment. If that is the case, SATDID will automatically generate the appropriate descriptive SATD comment that can be attached with the code. While our approach is applicable in principle to any type of code fragments, we focus in this study on technical debt hidden in conditional statements, one of the most TD-carrying parts of code. We explore and evaluate different implementations of SATDID. The evaluation results demonstrate the applicability and effectiveness of our framework over multiple benchmarks. Comparing with the results from the benchmarks, our approach provides at least 21.35, 59.36, 31.78, and 583.33% improvements in terms of Precision, Recall, F-1, and Bleu-4 scores, respectively. In addition, we conduct a human evaluation to the SATD comments generated by SATDID. In 1-5 and 0–5 scales for Acceptability and Understandability, the total means achieved by our approach are 3.128 and 3.172, respectively.

Is information always true? According to some authors, including Dretske, Grice, Barwise, and recently, Floridi, who has defended the Veridicality Thesis, the answer is positive. For, on Floridi's view, there is an intimate relation between information and knowledge, which is always true. It is argued in this article that information used in inferential knowledge can, nevertheless, be false, thereby showing that the Veridicality Thesis is false.

We provide a formal study of belief retraction operators that do not necessarily satisfy the (Inclusion) postulate. Our intuition is that a rational description of belief change must do justice to cases in which dropping a belief can lead to the inclusion, or 'liberation', of others in an agent's corpus. We provide two models of liberation via retraction operators: σ-liberation and linear liberation. We show that the class of σ-liberation operators is included in the class of linear ones and provide axiomatic characterisations for each class. We show how any retraction operator (including the liberation operators) can be 'converted' into either a withdrawal operator (i.e., satisfying (Inclusion)) or a revision operator via (a slight variant of) the Harper Identity and the Levi Identity respectively.

Traditional accounts of belief change have been criticized for placing undue emphasis on the new belief provided as input. A recent proposal to address such issues is a framework for non-prioritized belief change based on default theories (Ghose and Goebel, 1998). A novel feature of this approach is the introduction of disbeliefs alongside beliefs which allows for a view of belief contraction as independently useful, instead of just being seen as an intermediate step in the process of belief revision. This approach is, however, restrictive in assuming a linear ordering of reliability on the received inputs. In this paper, we replace the linear ordering with a preference ranking on inputs from which a total preorder on inputs can be induced. This extension brings along with it the problem of dealing with inputs of equal rank. We provide a semantic solution to this problem which contains, as a special case, AGM belief change on closed theories.

Purpose – Business process has become the core assets of many organizations and it becomes increasing common for most medium to large organizations to have collections of hundreds or even thousands of business process models. The purpose of this paper is to explore an alternative dimension to process mining in which the objective is to extract process constraints (or business rules) as opposed to business process models. It also focusses on an alternative data set – process models as opposed to process instances (i.e. event logs). Design/methodology/approach – The authors present a new method of knowledge discovery to find business activity sequential patterns embedded in process model repositories. The extracted sequential patterns are considered as business rules. Findings – The authors find significant knowledge hidden in business processes model repositories. The hidden knowledge is considered as business rules. The business rules extracted from process models are significant and valid sequential correlations among business activities belonging to a particular organization. Such business rules represent business constraints that have been encoded in business process models. Experimental results have indicated the effectiveness and accuracy of the approach in extracting business rules from repositories of business process models. Social implications – This research will assist organizations to extract business rules from their existing business process models. The discovered business rules are very important for any organization, where rules can be used to help organizations better achieve goals, remove obstacles to market growth, reduce costly mistakes, improve communication, comply with legal requirements, and increase customer loyalty. Originality/value – There has very been little work in mining business process models as opposed to an increasing number of very large collections of business process models. This work has filled this gap with the focus on extracting business rules.