Performance is arguably the most crucial attribute that reflects the quality of a configurable software system. However, given the increasing scale and complexity of modern software, modeling and predicting how various configurations can impact performance becomes one of the major challenges in software maintenance. As such, performance is often modeled without having a thorough knowledge of the software system, but relying mainly on data, which fits precisely with the purpose of deep learning.
In this paper, we conduct a comprehensive review exclusively on the topic of deep learning for performance learning of configurable software, covering 1,206 searched papers spanning six indexing services, based on which 99 primary papers were extracted and analyzed. Our results outline key statistics, taxonomy, strengths, weaknesses, and optimal usage scenarios for techniques related to the preparation of configuration data, the construction of deep learning performance models, the evaluation of these models, and their utilization in various software configuration-related tasks.We also identify the good practices and potentially problematic phenomena from the studies surveyed, together with a comprehensive summary of actionable suggestions and insights into future opportunities within the field. To promote open science, all the raw results of this survey can be accessed at our repository: https://github.com/ideas-labo/DCPL-SLR.
Context: The importance of defining learning outcomes and the planning stage for a systematic literature review. Objective: A protocol for carrying out a systematic literature review about the evidence for the tool support for the learning outcomes and the teaching-learning process using Bloom's taxonomy to address it. Method: The definition of a protocol to conduct a systematic literature review according to the guidelines of B. Kitchenham. Results: A validated protocol to conduct a systematic literature review. Conclusions: A proposal for the protocol definition of a systematic literature re…
IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection
Jheronimus Academy of Data Science（Jheronimus数据科学学院）；Tilburg University（蒂尔堡大学）；Eindhoven University of Technology（埃因霍温理工大学）；University of Sannio（萨诺尼大学）
Blockchain has been proposed to facilitate the enactment of interorganisational business processes. For such processes, blockchain can guarantee the enforcement of rules and the integrity of execution traces - without the need for a centralised trusted party. However, the enactment of interorganisational processes pose manifold challenges. In this work, we ask what answers the research field offers in response to those challenges. To do so, we conduct a systematic literature review (SLR). As our guiding question, we investigate the guarantees and capabilities of blockchain-based enactment appr…
Third-party libraries (TPLs) have been widely used in mobile apps, which play an essential part in the entire Android ecosystem. However, TPL is a double-edged sword. On the one hand, it can ease the development of mobile apps. On the other hand, it also brings security risks such as privacy leaks or increased attack surfaces (e.g., by introducing over-privileged permissions) to mobile apps. Although there are already many studies for characterizing third-party libraries, including automated detection, security and privacy analysis of TPLs, TPL attributes analysis, etc., what strikes us odd is…
Background: Good API documentation facilities the development process, improving productivity and quality. While the topic of API documentation quality has been of interest for the last two decades, there have been few studies to map the specific constructs needed to create a good document. In effect, we still need a structured taxonomy against which to capture knowledge. Aims: This study reports emerging results of a systematic mapping study. We capture key conclusions from previous studies that assess API documentation quality, and synthesise the results into a single framework. Method: By c…
Modern Code Review (MCR) is a widely known practice of software quality assurance. However, the existing body of knowledge of MCR is currently not understood as a whole. Objective: Our goal is to identify the state of the art on MCR, providing a structured overview and an in-depth analysis of the research done in this field. Method: We performed a systematic literature review, selecting publications from four digital libraries. Results: A total of 139 papers were selected and analyzed in three main categories. Foundational studies are those that analyze existing or collected data from the adop…
Blockchain is a disruptive technology intended at implementing secure decentralized distributed systems, in which transactional data can be shared, stored and verified by participants of a system using cryptographic and consensus mechanisms, elevating the need for a central authentication/verification authority. Contrary to the belief, blockchain-based systems are not inherently secure by design; it is crucial for security software engineers to be aware of the various blockchain specific architectural design decisions and choices and their consequences on the dependability of the software syst…
Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey
Huazhong University of Science and Technology（华中科技大学）；Monash University（莫纳什大学）；Ohio State University（俄亥俄州立大学）；Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）
Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an ADS or ADAS may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and valida…
Software Vulnerability Prediction (SVP) is a data-driven technique for software quality assurance that has recently gained considerable attention in the Software Engineering research community. However, the difficulties of preparing Software Vulnerability (SV) related data is considered as the main barrier to industrial adoption of SVP approaches. Given the increasing, but dispersed, literature on this topic, it is needed and timely to systematically select, review, and synthesize the relevant peer-reviewed papers reporting the existing SV data preparation techniques and challenges. We have ca…
Cognition plays a fundamental role in most software engineering activities. This article provides a taxonomy of cognitive concepts and a survey of the literature since the beginning of the Software Engineering discipline. The taxonomy comprises the top-level concepts of perception, attention, memory, cognitive load, reasoning, cognitive biases, knowledge, social cognition, cognitive control, and errors, and procedures to assess them both qualitatively and quantitatively. The taxonomy provides a useful tool to filter existing studies, classify new studies, and support researchers in getting fam…
As one of the crucial human aspects, individual decision-making behavior that may affect the quality of a software project is adaptive to the environment in which the individual is. However, no comprehensive reference framework of the environmental factors influencing individual decision-making behavior in software projects is presently available. This paper undertakes a systematic literature review (SLR) to gain insight into existing studies on this topic. After a careful SLR process, 40 studies were targeted to solve this question. Based on these extracted studies, we first provided a taxono…
A Systematic Review of Digital Twin-Driven Predictive Maintenance in Industrial Engineering: Taxonomy, Architectural Elements, and Future Research Directions
Intelligent Distributed Computing and Systems (INDUCE) Lab, Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al-Ain, United Arab Emirates, Emirates Center for Mobility Research, United Arab Emirates University, Al-Ain, United Arab Emirates（智能分布式计算与系统实验室，计算机科学与软件工程系，信息科技学院，阿联酋大学，阿恩，阿联酋，移动性研究中心，阿联酋大学，阿恩，阿联酋）；Department of Architectural Engineering, College of Engineering, United Arab Emirates University, Al-Ain, United Arab Emirates（建筑工程系，工程学院，阿联酋大学，阿恩，阿联酋）；Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al-Ain, United Arab Emirates（信息系统与安全系，信息科技学院，阿联酋大学，阿恩，阿联酋）；Department of Computer and Network Engineering, United Arab Emirates University, Al-Ain, United Arab Emirates（计算机与网络工程系，阿联酋大学，阿恩，阿联酋）
The Continuous software engineering is a collaborative software development environment which offers the continues development and deployment of quality software project within short time. The Continuous software engineering practices are not yet mature enough, and the software organizations hesitate to adopt it. This study aims: (1) to explore the Continuous software engineering challenges by conducting systematic literature review (SLR) and to get the insight of industry experts via questionnaire survey study; (2) to prioritize the investigated challenges using fuzzy analytical hierarchy pro…
Reducing cost and time required to build high quality software is a major goal for software developers. Building tools and techniques that can help achieve such a goal is the chief aim for Automated Software Engineering (ASE) researchers. However, in order to be effective an ASE researcher or professional must understand the characteristics of both successful and not-so-successful ASE tools, and the constituent techniques employed by such ASE tools. In this paper we present such a characterization of ASE tools and major constituent techniques from different areas of computer science and engine…
Autoscaling system can reconfigure cloud-based services and applications, through various configurations of cloud software and provisions of hardware resources, to adapt to the changing environment at runtime. Such a behavior offers the foundation for achieving elasticity in modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, cloud autoscaling system has been engineered as one of the most complex, sophisticated and intelligent artifacts created by human, aiming to achieve self-aware, self-adaptive and dependable runtime scaling. Yet, exis…
