Knowledge-intensive processes progress towards the achievement of operational goals. These processes typically rely on data to enable data-driven decision making, but also require substantial flexibility to deal with the complex and dynamic environments in which they operate. Consequently, declarative data-centric process modeling languages such as the Case Management Model and Notation (CMMN) have been proposed to model knowledge-intensive processes. However, while such process models allow to express goals, they specify dependencies between the goals only implicitly. This makes the goal-oriented behavior of declarative data-centric process models hard to understand, and therefore obfuscates the goal-oriented behavior of knowledge-intensive processes. This paper defines a structural, semi-automated approach to explicate the goal-oriented aspects of declarative data-centric process models. The approach first derives goal relations from a declarative data-centric process model and next synthesizes these goal relations into a goal model using an algorithm. The approach is supported by a tool and has been evaluated in case studies. Using the approach, implicit goal dependencies in declarative data-centric process models are expressed in goal models. This supports the understanding of goal-oriented aspects of declarative data-centric process models.

Achieving real-time agility and adaptation with respect to changing requirements in existing IT infrastructure can pose a complex challenge. We describe a goal-oriented approach to manage this complexity. We argue that a goal-oriented perspective can form an effective basis for devising and deploying responses to changed requirements at runtime. We offer an extended vocabulary of goal types by presenting two novel conceptions: differential goals and integral goals, which we formalize in both linear-time and branching-time settings. We describe goal lifecycles and interactions and the extended notion of context for the representation of rapidly changing, complex operating environments. We then illustrate the working of the approach by presenting a detailed scenario of adaptation in a Kubernetes setting, in the face of a Distributed Denial-of-Service (DDoS) attack.

Defects are common in software systems and cause many problems for software users. Different methods have been developed to make early prediction about the most likely defective modules in large codebases. Most focus on designing features (e.g. complexity metrics) that correlate with potentially defective code. Those approaches however do not sufficiently capture the syntax and multiple levels of semantics of source code, a potentially important capability for building accurate prediction models. In this paper, we report on our experience of deploying a new deep learning tree-based defect prediction model in practice. This model is built upon the tree-structured Long Short Term Memory network which directly matches with the Abstract Syntax Tree representation of source code. We discuss a number of lessons learned from developing the model and evaluating it on two datasets, one from open source projects contributed by our industry partner Samsung and the other from the public PROMISE repository.

Open Source Software Development (OSSD) often suffers from conflicting views and actions due to the perceived flat and open ecology of an open source community. This often manifests itself as a lack of codified knowledge that is easily accessible for community members. How decisions are made and expectations of a software system are often described in detail through the many forms of social communications that take place within a community. These social interactions form norms which are influential in dictating what behaviors are expected in a community and of the system. In this paper, we provide a tool which mines these social interactions (in the form of bug reports) and extract norms of the system, externalizing this information into a codified form that allows others within the community to be aware of without having witnessed the social interactions.

Identifying risks relevant to a software project and planning measures to deal with them are critical to the success of the project. Current practices in risk assessment mostly rely on high-level, generic guidance or the subjective judgements of experts. In this paper, we propose a novel approach to risk assessment using historical data associated with a software project. Specifically, our approach identifies patterns of past events that caused project delays, and uses this knowledge to identify risks in the current state of the project. A set of risk factors characterizing “risky” software tasks (in the form of issues) were extracted from five open source projects: Apache, Duraspace, JBoss, Moodle, and Spring. In addition, we performed feature selection using a sparse logistic regression model to select risk factors with good discriminative power. Based on these risk factors, we built predictive models to predict if an issue will cause a project delay. Our predictive models are able to predict both the risk impact (i.e. the extend of the delay) and the likelihood of a risk occurring. The evaluation results demonstrate the effectiveness of our predictive models, achieving on average 48%-81% precision, 23%-90% recall, 29%-71% F-measure, and 70%-92% Area Under the ROC Curve. Our predictive models also have low error rates: 0.39-0.75 for Macro-averaged Mean Cost-Error and 0.7-1.2 for Macro-averaged Mean Absolute Error.

This paper presents a method of ontology-based sentiment classification to classify and analyse online product reviews of consumers. We implement and experiment with a support vector machines text classification approach based on a lexical variable ontology. After testing, it could be demonstrated that the proposed method can provide more effectiveness for sentiment classification based on text content.

Unlike a traditional software module, which runs within a predictable domain, Web services are autonomous software agents running in a heterogeneous execution environment. Because of distributed responsibilities, ownership and control, it is often not feasible to acquire all information needed for the service composition. These characteristics of autonomy and heterogeneity are fundamental to service oriented computing but make it inherently difficult to avoid service conflicts. To reason about and adapt to a changing environment, in this work, we extend current OWL-S by introducing the concept of service assumptions which allow reasoning with incomplete information. Furthermore, together with the proposed service assumptions, a sequence of rules is proposed to describe all permitted behaviors in service composition context

Inconsistency handling is an aspect of requirements engineering that has attracted considerable research attention. We explore novel ways to applying semantic Web technologies to this problem, in the context of a Web-based agent-mediated environment for distributed requirements engineering.