A review of software change impact analysis

Change impact analysis is required for constantly evolving systems to support the comprehension, implementation, and evaluation of changes. A lot of research effort has been spent on this subject over the last twenty years, and many approaches were published likewise. However, there has not been an extensive attempt made to summarize and review published approaches as a base for further research in the area. Therefore, we present the results of a comprehensive investigation of software change impact analysis, which is based on a literature review and a taxonomy for impact analysis. The contribution of this review is threefold. First, approaches proposed for impact analysis are explained regarding their motivation and methodology. They are further classified according to the criteria of the taxonomy to enable the comparison and evaluation of approaches proposed in literature. We perform an evaluation of our taxonomy regarding the coverage of its classification criteria in studied literature, which is the second contribution. Last, we address and discuss yet unsolved problems, research areas, and challenges of impact analysis, which were discovered by our review to illustrate possible directions for further research

An Algebra of Design Patterns

In a pattern-oriented software design process, design decisions are made by selecting and instanti- ating appropriate patterns, and composing them together. In our previous work, we enabled these decisions to be formalised by dening a set of operators on patterns with which instantiations and compositions can be represented. In this paper, we investigate the algebraic properties of these operators. We provide and prove a complete set of algebraic laws so that equivalence between pattern expressions can be proven. Furthermore, we dene an always-terminating normalisation of pattern expressions to a canonical form, which is unique modulo equivalence in rst-order logic. By a case study, the pattern-oriented design of an extensible request-handling framework, we demonstrate two practical applications of the algebraic framework. Firstly, we can prove the correctness of a nished design with respect to the design decisions made and the formal specication of the patterns. Secondly, we can even derive the design from these components

Digital Twin in the IoT context: a survey on technical features, scenarios and architectural models

Digital Twin is an emerging concept that is gaining attention in various industries. It refers to the ability to clone a physical object into a software counterpart. The softwarized object, termed logical object, reflects all the important properties and characteristics of the original object within a specific application context. To fully determine the expected properties of the Digital Twin, this paper surveys the state of the art starting from the original definition within the manufacturing industry. It takes into account related proposals emerging in other fields, namely, Augmented and Virtual Reality (e.g., avatars), Multi-agent systems, and virtualization. This survey thereby allows for the identification of an extensive set of Digital Twin features that point to the “softwarization” of physical objects. To properly consolidate a shared Digital Twin definition, a set of foundational properties is identified and proposed as a common ground outlining the essential characteristics (must-haves) of a Digital Twin. Once the Digital Twin definition has been consolidated, its technical and business value is discussed in terms of applicability and opportunities. Four application scenarios illustrate how the Digital Twin concept can be used and how some industries are applying it. The scenarios also lead to a generic DT architectural Model. This analysis is then complemented by the identification of software architecture models and guidelines in order to present a general functional framework for the Digital Twin. The paper, eventually, analyses a set of possible evolution paths for the Digital Twin considering its possible usage as a major enabler for the softwarization process

Participant Domain Name Token Profile for security enhancements supporting service oriented architecture

This research proposes a new secure token profile for improving the existing Web Services security standards. It provides a new authentication mechanism. This additional level of security is important for the Service-Oriented Architecture (SOA), which is an architectural style that uses a set of principles and design rules to shape interacting applications and maintain interoperability. Currently, the market push is towards SOA, which provides several advantages, for instance: integration with heterogeneous systems, services reuse, standardization of data exchange, etc. Web Services is one of the technologies to implement SOA and it can be implemented using Simple Object Access Protocol (SOAP). A SOAP-based Web Service relies on XML for its message format and common application layer protocols for message negotiation and transmission. However, it is a security challenge when a message is transmitted over the network, especially on the Internet. The Organization for Advancement of Structured Information Standards (OASIS) announced a set of Web Services Security standards that focus on two major areas. “Who” can use the Web Service and “What” are the permissions. However, the location or domain of the message sender is not authenticated. Therefore, a new secure token profile called: Participant Domain Name Token Profile (PDNT) is created to tackle this issue. The PDNT provides a new security feature, which the existing token profiles do not address. Location-based authentication is achieved if adopting the PDNT when using Web Services. In the performance evaluation, PDNT is demonstrated to be significantly faster than other secure token profiles. The processing overhead of using the PDNT with other secure token profiles is very small given the additional security provided. Therefore all the participants can acquire the benefits of increased security and performance at low cost

Assessing Comment Quality in Object-Oriented Languages

Previous studies have shown that high-quality code comments support developers in software maintenance and program comprehension tasks. However, the semi-structured nature of comments, several conventions to write comments, and the lack of quality assessment tools for all aspects of comments make comment evaluation and maintenance a non-trivial problem. To understand the specification of high-quality comments to build effective assessment tools, our thesis emphasizes acquiring a multi-perspective view of the comments, which can be approached by analyzing (1) the academic support for comment quality assessment, (2) developer commenting practices across languages, and (3) developer concerns about comments. Our findings regarding the academic support for assessing comment quality showed that researchers primarily focus on Java in the last decade even though the trend of using polyglot environments in software projects is increasing. Similarly, the trend of analyzing specific types of code comments (method comments, or inline comments) is increasing, but the studies rarely analyze class comments. We found 21 quality attributes that researchers consider to assess comment quality, and manual assessment is still the most commonly used technique to assess various quality attributes. Our analysis of developer commenting practices showed that developers embed a mixed level of details in class comments, ranging from high-level class overviews to low-level implementation details across programming languages. They follow style guidelines regarding what information to write in class comments but violate the structure and syntax guidelines. They primarily face problems locating relevant guidelines to write consistent and informative comments, verifying the adherence of their comments to the guidelines, and evaluating the overall state of comment quality. To help researchers and developers in building comment quality assessment tools, we contribute: (i) a systematic literature review (SLR) of ten years (2010–2020) of research on assessing comment quality, (ii) a taxonomy of quality attributes used to assess comment quality, (iii) an empirically validated taxonomy of class comment information types from three programming languages, (iv) a multi-programming-language approach to automatically identify the comment information types, (v) an empirically validated taxonomy of comment convention-related questions and recommendation from various Q&A forums, and (vi) a tool to gather discussions from multiple developer sources, such as Stack Overflow, and mailing lists. Our contributions provide various kinds of empirical evidence of the developer’s interest in reducing efforts in the software documentation process, of the limited support developers get in automatically assessing comment quality, and of the challenges they face in writing high-quality comments. This work lays the foundation for future effective comment quality assessment tools and techniques

Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications

Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies

A Digital Game Maturity Model

Game development is an interdisciplinary concept that embraces artistic, software engineering, management, and business disciplines. Game development is considered as one of the most complex tasks in software engineering. Hence, for successful development of good-quality games, the game developers must consider and explore all related dimensions as well as discussing them with the stakeholders involved. This research facilitates a better understanding of important dimensions of digital game development methodology. The increased popularity of digital games, the challenges faced by game development organizations in developing quality games, and severe competition in the digital game industry demand a game development process maturity assessment. Consequently, this study presents a Digital Game Maturity Model to evaluate the current development methodology in an organization. The objective is first to identify key factors in the game development process, then to classify these factors into target groups, and eventually to use this grouping as a theoretical basis for proposing a maturity model for digital game development. In doing so, the research focuses on three major stakeholders in game development: developers, consumers, and business management. The framework of the proposed model consists of assessment questionnaires made up of key identified factors from three empirical studies, a performance scale, and a rating method. The main goal of the questionnaires is to collect information about current processes and practices. This research contributes towards formulating a comprehensive and unified strategy for game development process maturity assessment. The proposed model was evaluated with two case studies from the digital game industry

Path-based dynamic impact analysis

Successful software systems evolve over their lifetimes through the cumulative changes made by software maintainers. As software evolves, the problems resulting from software change worsen, exacerbated by increased system size and complexity, lack of program understanding, amount of effort required to make changes, and number of personnel involved. Experience shows that software changes made without visibility into their effects can lead to poor effort estimates, delays in release schedules, degraded software design, unreliable software products, increased costs, and premature retirement of the software system. Software change impact analysis, impact analysis, is a software maintenance technique meant to address these problems, by assessing the effects of changes made to a software system. While impact analysis is frequently cited as a motivation or a potential application for program analysis and software maintenance research, research specific to the task of impact analysis has languished for more than 10 years. In addition, few researchers have examined the empirical factors underlying common impact analysis techniques or the tradeoffs inherent in known techniques, and none have performed empirical studies comparing impact analysis techniques. In this dissertation we introduce a new impact analysis approach, named PathImpact, that addresses a set of tradeoffs not addressed by any current impact analysis approach. Ours is the first fully-dynamic impact analysis approach. PathImpact uses light-weight instrumentation to record program execution at the level of procedure calls and returns, then efficiently builds a compressed representation that can be directly used to estimate change impact. We next extend PathImpact to accomodate system evolution yielding a technique we call EvolveImpact. EvolveImpact updates the impact representation after a system change, whereas PathImpact requires a complete recompution. In addition, we show how our approaches can be extended to a large class of emerging software architectures, including Java component-based systems and large-scale systems. Finally, we discuss the implementation of our approaches, present the first cost models for impact analysis techniques, and report the results of the first empirical studies that compare impact analysis techniques. We also empirically examine the performance of our approaches and the factors affecting the use of our techniques in practice. We found that our approach has linear time and space complexity (in the size of the dynamic information collected) and achieved a mean compression value of 0.955 on the subjects we used in our experiments. Our investigation of program evolution across multiple versions of three of our subject programs showed that, depending on the level of change activity, EvolveImpact can update the impact representation more efficiently than recomputing it in a majority of cases