Process Coordination with Business Artifacts and Multiagent Technologies

Coding style is an important factor in measuring the program quality. The paper explores on the C program coding style of the program annotation writing, data declaration, program statement writing, and input/output design style from the perspective of software engineering. Good coding style plays an important role in improving the readability, testability and maintainability of the program.

The goal of this article is to provide a rigorous conceptual framework for defining the concepts of dependability and resilience. Since the seventies, the terms dependability and resilience have been used in nearly all the scientific and technological fields related to Information and Communication systems. The introduction and use of these concepts in all these fields makes it difficult to have a common and precise definition. Having such a definition is nevertheless mandatory for the software and systems engineering research community that create development processes, languages and tools to support the engineering of products that would be required to be dependable or resilient. For this, we introduce an abstract and generic terminology defined mathematically to be used when speaking about dependability and resiliency. We also provide some abstract semantic descriptions to these terminological elements. This formal framework is defined from a software engineering perspective, which means that we define its components such that they are useful for the development or improvement of analysis, architectural design, detailed design, implementation, verification and maintenance phases. To this aim, we provide the necessary elements in accordance with a model driven engineering perspective that enable the definition of a new modelling language for dependable and resilient systems.

A Secure Healthcare System: From Design to Implementation

The recent release of ChatGPT conversational agent has been a surprise to me, and to many of my colleagues from the software engineering community. Progress goes extremely fast, while it appears to be a true "game-changing" technology that can even generate programs and fix bugs. Machine Learning (ML) provides engineers with the prospect of producing data-driven software, with little manual code writing. These ML-enabled software bring us to a new era where systems’ logic is automatically produced from data, with a small amount of human-written code. Would we trust such software mixing ML and regular code, would you rely on it and under which conditions? This is still too early to answer these questions, and a challenging direction to explore.This radical change questions the way software are engineered, validated, secured, deployed and maintained. The overall challenge is thus to automate these activities accounting for the statistical nature of ML-enabled software.Taking a software engineering perspective, and starting from a concrete case from the finance industry, the presentation will focus on testing and robustifying a ML model which is integrated in a larger software system that takes as input domain objects (e.g. financial transaction, malware, network traffic). One traditional way to robustify a ML model consists in generating adversarial inputs, e.g. leading to a misclassification, and retraining the model. Indeed, despite their impressive performance, ML models are sensitive to small perturbations in the input. The resulting adversarial inputs raise multiple questions about the robustness of such systems, especially in safety- and business-critical domains. However, the generation of feasible, exploitable adversarial test examples is challenging, as they must satisfy the business logic constraints over the feature space. We analyse the limitations of current adversarial approaches and explore new algorithms that combine multi-objective search with constraint-solving techniques. While the attack part is the offensive weapon, we also consider the challenge to efficiently shield (e.g. repair) the systems against such threats, and finally end the seminar by mentioning other research directions to deploy robust ML-enabled systems.

Context: Using student subjects in empirical studies has been discussed extensively from a methodological perspective in Software Engineering (SE), but there is a lack of similar discussion surrounding ethical aspects of doing so. As students are in a subordinate relationship to their instructors, such a discussion is needed. Objective: We aim to increase the understanding of practices and perceptions SE researchers have of ethical issues with student participation in empirical studies. Method: We conducted a systematic mapping study of 372 empirical SE studies involving students, following up with a survey answered by 100 SE researchers regarding their current practices and opinions regarding student participation. Results: The mapping study shows that the majority of studies does not report conditions regarding recruitment, voluntariness, compensation, and ethics approval. In contrast, the majority of survey participants supports reporting these conditions. The survey further reveals that less than half of the participants require ethics approval. Additionally, the majority of participants recruit their own students on a voluntary basis, and use informed consent with withdrawal options. There is disagreement among the participants whether course instructors should be involved in research studies and if should know who participates in a study. Conclusions: It is a positive sign that mandatory participation is rare, and that informed consent and withdrawal options are standard. However, we see immediate need for action, as study conditions are under-reported, and as opinions on ethical practices differ widely. In particular, there is little regard in SE on the power relationship between instructors and students.

This chapter reviews the notions of security and dependability properties from the perspective of software engineering, providing the reader with a technical background on dynamic verification and runtime monitoring techniques. The chapter covers the technical background on security and dependability properties with system verification through dynamic verification or monitoring. The authors initially provide a short overview of the security and dependability properties themselves. Once definitions of security and dependability properties are introduced, they present a critical analysis of current research on dynamic verification by presenting general purpose and security oriented dynamic verification approaches.

In order to overcome the limitations of traditional software metrics and meet the urgent demand in large- scale software development, in this study we constructed the static structure model for large-scale software by structural mapping and visualization. In particular, through application of the complex networks theory, we proposed a two-dimension metrics suite based on complex networks parameters from the perspective of software engineering. The feasibility and validity of the metrics were testified through statistical analyzing the data from a set of software projects; and these experiments show that the metrics suite can not only uncover the features of software structure such as scale free, small world, but also can get some latent rules about design and structure. These features can be conveniently used by designers and developers to understand the system, to control complexity and to improve process.

Key-Value stores (KVSs) are the most flexible and simplest model of NoSQL databases, which have become highly popular over the last few years due to their salient features such as availability, portability, reliability, and low operational cost. From the perspective of software engineering, the chief obstacle for KVSs is to achieve software quality attributes (consistency, throughput, latency, security, performance, load balancing, and query processing) to ensure quality. The presented research is a Systematic Literature Review (SLR) to find the state-of-the-art research in the KVS domain, and through doing so determine the major challenges and solutions. This work reviews the 45 papers between 2010–2018 that were found to be closely relevant to our study area. The results show that performance is addressed in 31% of the studies, consistency is addressed in 20% of the studies, latency and throughput are addressed in 16% of the studies, query processing is addressed in 13% of studies, security is addressed in 11% of the studies, and load balancing is addressed in 9% of the studies. Different models are used for execution. The indexing technique was used in 20% of the studies, the hashing technique was used in 13% of the studies, the caching and security techniques were used together in 9% of the studies, the batching technique was used in 5% of the studies, the encoding techniques and Paxos technique were used together in 4% of the studies, and 36% of the studies used other techniques. This systematic review will enable researchers to design key-value stores as efficient storage. Regarding future collaborations, trust and privacy are the quality attributes that can be addressed; KVS is an emerging facet due to its widespread popularity, opening the way to deploy it with proper protection.

In Engineering and Scientific computing, a significant number of software systems that are being commonly used by the Engineering community are developed and deployed as Open Source Systems (OSS). This collaborative development model has been widely accepted in both academia and industry. As these systems are typically developed by developers who are experts in their fields (e.g., Engineering), some of those programmers may be lacking an essential skillset today's software quality and engineering concepts (e.g., source code maintainability and analyzability, source code vulnerability, parallelization, etc.). An empirical study is presented that examines some of the source code quality aspects and programming practices (e.g., jumps statements, usage of recursion) from software engineering perspective in ten open source Engineering software systems. The studied systems are written in the C/C++ programming languages and comprised of over ten million lines of code, and present numerous tasks of the engineering community, including computer-aided design, computational fluid dynamics, and finite element analysis. Static analysis methods are applied to each system to determine the number of jump statements (Goto and Break statements) and functions that are recursively invoked. Functions that contain direct or indirect recursive calls are determined and counted. The results show that number of the used jump statements and functions that have recursive calls are significant and thus pose more of a roadblock to effectively and automatically maintain, comprehend, analyze, and re-engineer systems. Lastly, this leads to inefficiencies in a software engineer's ability to accomplish these common tasks. The study showed that issues in source code stemming from the lack of coding standards have increased over time and are prevalent.

App store analysis has become an important discipline in recent software engineering research. It empirically studies apps using information mined from their distribution platforms. Information provided by users, such as app reviews, are of high interest to developers. Commercial providers such as App Annie analyzing this information became an important source for companies developing and marketing mobile apps. In this paper, we perform an exploratory study, which analyzes over seven million reviews from the Apple AppStore regarding their emotional sentiment. Since recent research in this field used sentiments to detail and refine their results, we aim to gain deeper insights into the nature of sentiments in user reviews. In this study we try to evaluate whether or not the emotional sentiment can be an informative feature for software engineers, as well as pitfalls of its usage. We present our initial results and discuss how they can be interpreted from the software engineering perspective.

A software engineering perspective on environmental modeling framework design: The Object Modeling System

Virtual Reality (VR) is a computer technology that holds the promise of revolutionizing the way we live. The release in 2016 of new-generation headsets from Facebook-owned Oculus and HTC has renewed the interest in that technology. Thousands of VR applications have been developed over the past years, but most software developers lack formal training on this technology. In this paper, we propose descriptive information on the state of practice of VR applications' development to understand the level of maturity of this new technology from the perspective of Software Engineering (SE). To do so, we focused on the analysis of 320 VR open source projects from Github to determine which are the most popular languages and engines used in VR projects, and evaluate the quality of the projects from a software metric perspective. To get further insights on VR development, we also manually analyzed nearly 300 questions from Stack Overflow. Our results show that (1) VR projects on GitHub are currently mostly small to medium projects, and (2) the most popular languages are JavaScript and C#. Unity is the most used game engine during VR development and the most discussed topic on Stack Overflow. Overall, our exploratory study is one of the very first of its kind for VR projects and provides material that is hopefully a starting point for further research on challenges and opportunities for VR software development.

When following the principles of value-based software engineering, business, customer satisfaction, and engineering considerations need to be balanced to develop and operate the software so that it satisfies the different stakeholders' expectations. This, however, requires knowing the relevant quality metrics covering these value-oriented expectations and potential sources for their measurement. In this work, a categorisation of value-oriented quality metrics that are practically relevant is presented. Therefore, the authors conducted an online survey with practitioners who assessed the relevance of 61 value-oriented metrics, gathered from a preceding systematic mapping study. The authors grouped these metrics into 10 categories, based on financial, customer satisfaction, value proposition, and creation perspectives. Also, the authors examined the frequency of particular steps at which these measures accrue and identified their most relevant data sources. The participants rated metrics for feature reliability, performance, as well as test and development efficiency as most relevant for value orientation. According to the participants, the authors' collection covers all relevant metrics for addressing financial and market and feature usability aspects. The authors' categorisation and the metrics' relevance assessments shall support software engineers in selecting relevant metrics and their sources for software product development.

App store analysis has become an important discipline in recent software engineering research. It empirically studies apps using information mined from their distribution platforms. Information provided by users, such as app reviews, are of high interest to developers. Commercial providers such as App Annie analyzing this information became an important source for companies developing and marketing mobile apps. In this paper, we perform an exploratory study, which analyzes over seven million reviews from the Apple AppStore regarding their emotional sentiment. Since recent research in this field used sentiments to detail and refine their results, we aim to gain deeper insights into the nature of sentiments in user reviews. In this study we try to evaluate whether or not the emotional sentiment can be an informative feature for software engineers, as well as pitfalls of its usage. We present our initial results and discuss how they can be interpreted from the software engineering perspective.

Today, thanks to advances in cloud computing, it is possible for small teams of software developers to produce internet-scale products, a feat that was previously the preserve of large organizations. Herein, we describe how these advances in software engineering can be made more readily available to bioinformaticians. In the same way that cloud computing has democratized access to distributed systems engineering for generalist software engineers, access to scalable and reproducible bioinformatic engineering can be democratized for generalist bioinformaticians and biologists. We present solutions, based on our own efforts, to achieve this goal.