Requirement Analysis and System Synthesis Technique for FIC Applications

The C* Conference on Computer Science and Software Engineering (C3S2E) was established in 2008. C3S2E 2010 is the third annual meeting in the C3S2E conference series and is held in Montréal, Québec, Canada, during May 19-20, 2010. The conference aims to address the need of the academic community in computer science and software engineering. It brings together academics, government and industry professionals to discuss recent progress and challenges in a broad range of areas in the field of computer science and software engineering such as algorithms and theory, artificial intelligence, bioinformatics, computer architecture, computer graphics, computer security, computer vision, data mining, databases, image processing, information retrieval, machine learning, as well as software engineering. C3S2E 2010 also serves as a platform for theoreticians and practitioners to exchange their original research ideas on academic or application aspects of computing and engineering of software systems, present their new findings on theoretical or practical aspects of computer science and software engineering, share their experiences on integrating new technologies into products and applications, discuss their work on applying computing and software engineering to real-life situations, describe their development and operations of challenging computing related systems, and identify unsolved challenges. Although it is the third time C3S2E is held, this comprehensive conference has grown from a Canadian conference into an international conference. For C3S2E 2010, we continue to keep our tradition for inviting international experts in various areas of computer science and software engineering to join our team of international program committee. As a result, our Program Committee consists of professionals from different parts of the world including Australia, Canada, Germany, Hong Kong, Italy, Japan, Portugal, South Korea, Turkey, UAE, and UK. This committee has done an excellent job in finishing the single-blind review and on-line double-blind debate processes in record time. On average, about 90% of reviews were submitted by the due date and about 3.67 reviews were received for each paper. In terms of submissions, we have attracted many high-quality papers submitted by authors globally. The paper selection process was thorough and competitive. On average, each paper was refereed by at least three reviewers, and about 18% of submissions were accepted as full research papers. This year, we have an intensive program that spans over two days. In addition to research papers and student posters, we also include position papers, which allow authors to give their opinions and vision on exploring new concepts and research directions.

Since the term “software engineering” was coined some 20+ years ago, [4] a number of definitions for both the practice and the practitioner, a “software engineer,” have been proposed. The definition from a recent report on undergraduate software engineering curricula [3] states:Software engineering entails the understanding and application of engineering principles, design skills, good management practice, computer science and mathematical formalism. …The Software Engineer must be able to estimate the cost and duration of the software development process, and to determine correctness and reliability and express them with a degree of precision meaningful to other engineers and informed clients. …In recent years, several advanced degree programs in “software engineering” have been instituted in the US and abroad. One example of this can be found at the SEI itself. Two of the goals of the SEI Education Program are to: (1) develop software engineering curriculum modules that identify, organize, and document the body of knowledge that could be taught in software engineering degree programs, and (2) design, develop, support, and maintain model curricula for undergraduate and graduate software engineering programs.The question remains, however, how much of this can be taught in a classroom setting or even in a design lab, and how much must be learned on the job or via some sort of apprentice program? What does Ada have to do with software engineering ? Can Ada exist without it? Can software engineering be done without Ada?There are those who believe the term “software engineering” is just plain wrong: [1]… I deeply resent the misuse of the word “engineering” in the compound term “software engineering.” Such misuse constitutes, in my opinion, a cheap, deceptive attempt to take advantage of the professional reputation and image developed by real engineers over a century or so … it constitutes theft of professional reputation.In 1986, I coordinated a similar panel for the ACM Computer Science Conference (CSC) [2]. The introduction from that session still holds true for this one, and is paraphrased and tuned below:Most universities prepare students for continued growth and development during a lifetime of employment, whether in an industrial or government setting. Since traditionally, such an education has implied better job opportunities, the university student believes that by studying computer science and/or software engineering, she2 will be assured of a good job at a reasonable salary performing interesting and challenging tasks.On the other hand, some universities prepare students for graduate education and research, placing a heavy emphasis on theory. From this perspective, the university student believes that upon completion of a computer science or software engineering degree, she will be able to choose where to pursue her research, at the university, industry, or government site of her choosing.Three years have passed; and I have found other group of articulate people to join us and present new and exciting viewpoints on the topic of software engineering, university education, and, of course Ada. Richard Fairley, who participated in that CSC panel, continues to represent the academic point of view; Marcia Finfer represents the industrial point of view; Garth Glynn also provides the industrial point of view with the interesting addition of evaluating graduate-level software engineering degree curricula; David Markus brings the perspective of the human resource issues; Lyn Plinta provides the view of the recent graduate dealing with veterans of the hacking era; and David Umphress represents the DoD interests.These panelists, bringing together a wealth of experience as educators, practitioners, and policy makers, provide insights from both the demand-side of software engineering and the supply-side, as they take a multi-faceted, multi-national look at industrial, academic, and government expectations of software engineering as it is taught and practiced today.

This white paper argues that formal methods need to be better rooted in higher education curricula for computer science and software engineering programmes of study. To this end, it advocates improved teaching of formal methods; systematic highlighting of formal methods within existing, ‘classical’ computer science courses; and the inclusion of a compulsory formal methods course in computer science and software engineering curricula. These recommendations are based on the observations that formal methods are an essential and cost-effective means to increase software quality; however computer science and software engineering programmes typically fail to provide adequate training in formal methods; and thus there is a lack of computer science graduates who are qualified to apply formal methods in industry. This white paper is the result of a collective effort by authors and participants of the 1st International Workshop on Formal Methods – Fun for Everybody which was held in Bergen, Norway, 2–3 December 2019. As such, it represents insights based on learning and teaching computer science and software engineering (with or without formal methods) at various universities across Europe.

The goals of computer science department and software engineering courses are not always clear as they could be, and exactly how much of a computer science curriculum should be spent on software engineering topics at the expense of topics with more immediate relevance to computer science majors is far from clear. Computer science students are sometimes skeptical about the relevance of software engineering course topics, particularly those who have had some experience in industry. They report that the techniques and procedures covered in standard software engineering texts are seldom used in industry. There is not enough communication between Industry and Academia, and graduates often must be re-trained for their new jobs. This is a proposal for a modification in the approach to software engineering education for computer science majors, and for clarification of the goals of computer science programs, and calls for procedures to increase the relevancy of computer science and software engineering courses to meet the needs of industry.

Software is the object of study in software engineering and computer science. Software is an intellectual artifact that provides a solution for a repeatable computer application, which enables existing tasks to be done easier, faster, and smarter, or which provides innovative applications for the industries and daily life. This paper attempts to explore the basic characteristics of software and to search the informatics and cognitive foundations of software in supplement to theories of computer science. In this paper, the nature of software is characterized by its informatics, behavioral, mathematical, and cognitive properties. Then, the cognitive informatics foundations of software engineering are developed on the basis of the informatics laws of software and software engineering psychology.

Software testing is one of the crucial supporting processes of the software life cycle. Unfortunately for the software industry, the role is stigmatized, partly due to misperception and partly due to treatment of the role. The present study aims to analyze the situation to explore what restricts computer science and software engineering students from taking up a testing career in the software industry. To conduct this study, we surveyed 88 Pakistani students taking computer science or software engineering degrees. The results showed that the present study supports previous work into the unpopularity of testing compared to other software life cycle roles. Furthermore, the findings of our study showed that the role of tester has become a social role, with as many social connotations as technical implications.

Message Sequence Charts (MSCs) is a standardized visual notation for expressing requirements in communicating systems. MSCs are commonly used to specify scenarios describing message exchange among entities such as objects, components, systems, etc. A multiplicity is a modeling construct that is used to define constraints in the existential relationships between entities and it is frequently found in static models, such as class or entity diagrams. This paper describes a formal approach for formalizing the combination of MSCs with multiplicities. The formalization is achieved by defining an instantiation partial function that provides a clear interpretation of how to instantiate MSCs with multiplicities. The formalization uses composition operators that are used to formalize inline expressions (a well-known MSCs modeling construct). This formalization is designed to allow integration with the current formal semantics proposed for MSCs

For decades, Lawrence Livermore National Laboratory (LLNL) has been engaged in significant research, development, and support for software to enable scientific computing and, particularly, the use of high performance computing (HPC) in the NNSA mission space. In particular, the move in the mid-1990’s to simulation as a leading component of stockpile stewardship through the ASCI and the successor ASC programs, as well as the need for reliable data acquisition and control software for the National Ignition Facility, have been important drivers in building expertise in production-quality software development at LLNL. LLNL has also been a leader in the DOE SciDAC FASTMath Institute and the DOE Exascale Computing Project (ECP), both of which have striven to make scientific computing software – in particular, the enabling technologies underpinning simulation capabilities – more widely adopted and sustainable. As such, we believe that our experience can inform the broader goal of software stewardship for scientific and high-performance computing. LLNL strongly supports the formation of a new DOE ASCR program element in software stewardship and sustainment. Historically, DOE ASCR has funded applied mathematics and computer science research that has led to the development of important new capabilities and algorithms that are expressed as artifacts in research software. Such frameworks, libraries, and tools have seldom been directly funded to address the important issues of code maintenance, documentation, robustness, and community building. Software engineering and support have typically been done on the side in support of the ASCR-driven research products. DOE funding priorities have been slow to recognize that good software engineering, the kind that ensures research investments have more adoption and longevity, requires significant resources. Based upon our experiences, we have prepared this response to highlight the concerns and issues we believe to be important as DOE ASCR considers its role in scientific software stewardship. We believe that role is important and will require a significant investment of new funding to legitimately support the technologies past and future DOE ASCR investments have and will produce to facilitate their uptake and adoption in the broader scientific computing community. Following a summary of our involvement in scientific software development, the remainder our response is organized around the nine topics specifically identified in the RFI.

Semantics is the meaning of symbols, notations, concepts, functions, and behaviors, as well as their relations that can be deduced onto a set of predefined entities and/or known concepts. Semantic computing is an emerging computational methodology that models and implements computational structures and behaviors at semantic or knowledge level beyond that of symbolic data. In semantic computing, formal semantics can be classified into the categories of to be, to have, and to do semantics. This paper presents a comprehensive survey of formal and cognitive semantics for semantic computing in the fields of computational linguistics, software science, computational intelligence, cognitive computing, and denotational mathematics. A set of novel formal semantics, such as deductive semantics, concept-algebra-based semantics, and visual semantics, is introduced that forms a theoretical and cognitive foundation for semantic computing. Applications of formal semantics in semantic computing are presented in case studies on semantic cognition of natural languages, semantic analyses of computing behaviors, behavioral semantics of human cognitive processes, and visual semantic algebra for image and visual object manipulations.

Software lifecycles are becoming an increasingly important issue for computational science & engineering (CSE) software. The process by which a piece of CSE software begins life as a set of research requirements and then matures into a trusted high-quality capability is both commonplace and extremely challenging. Although an implicit lifecycle is obviously being used in any effort, the challenges of this process-respecting the competing needs of research vs. production-cannot be overstated. Here we describe a proposal for a well-defined software life-cycle process based on modern Lean/Agile software engineering principles. What we propose is appropriate for many CSE software projects that are initially heavily focused on research but also are expected to eventually produce usable high-quality capabilities. The model is related to TriBITS, a build, integration and testing system, which serves as a strong foundation for this lifecycle model, and aspects of this lifecycle model are ingrained in the TriBITS system. Indeed this lifecycle process, if followed, will enable large-scale sustainable integration of many complex CSE software efforts across several institutions.

Requirements engineering, the first phase of any software development project, is the Achilles' heel of the whole development process, as requirements documents are often inconsistent and incomplete. In industrial requirements documents, natural language is the main presentation means. This results in the fact that the requirements documents are imprecise, incomplete, and inconsistent. A viable way to detect inconsistencies and omissions in documents is to extract system models from them. In our previous work we developed approaches translating textual scenarios to message sequence charts (MSCs) and textual descriptions of automata to automata themselves. It turned out that these approaches are highly sensitive to proper definition of terms (communicating objects for MSCs, states for automata). The goal of the presented paper is a systematic comparison of different term extraction heuristics, as a preliminary stage of MSC or automata extraction. The extracted terms were declared to communicating objects (in the case of MSCs) or to states (in the case of automata). The heuristics were compared on the basis of correctness of resulting MSCs and automata. We came to the conclusion that named entity recognition is the best performing technique for term extraction from requirements documents.

Software testing is one of the crucial supporting processes of the software life cycle. Unfortunately for the software industry, the role is stigmatized, partly due to misperception and partly due to treatment of the role. The present study aims to analyse the situation to explore what restricts computer science and software engineering students from taking up a testing career in the software industry. To conduct this study, we surveyed 88 Pakistani students taking computer science or software engineering degrees. The results showed that the present study supports previous work into the unpopularity of testing compared to other software life cycle roles. Furthermore, the findings of our study showed that the role of tester has become a social role, with as many social connotations as technical implications.

We live in exceptional times in which the entire world is witnessing the exponential spread of a pandemic, which requires to adopt new habits of mind and behaviors. In this paper, I introduce the term exponential competence, which encompasses these cognitive and social skills, and describe a course for computer science and software engineering students in which emphasis is placed on exponential competence. I argue that exponential competence is especially important for computer science and software engineering students, since many of them will, most likely, be required to deal with exponential phenomena in their future professional development.

Introduction The computing sciences are complex fields that combine both theoretical and practical components. Students successfully completing an undergraduate computer science program should have instruction in both the mathematical and theoretical foundations of computing as well as the more practical aspects of how to effectively use computers to solve problems. Software engineering, as its name implies, is more directed toward the practical aspects of how to successfully develop complex systems that meet user requirements and are reliable, usable, and maintainable. Computing Curricula 2005 recognizes the need for more extensive education to produce professional engineers than what can be reasonably provided in a typical computer science program (ACM, 2005). As such, they propose engineering be treated as a totally separate discipline within computer science education. However, while there has been some debate about the exact role engineering should have in a computer science program (Curran, 2003), the ACM Curricula has maintained the importance of engineering to all computer science students and has kept it as a core element in computer science education (ACM, 2008). For purposes of this paper, we will focus on the teaching of engineering within the computer science discipline. Unlike other topics in the computer science major, the techniques and principles taught in engineering are often first developed and refined in industry before arriving in the classroom. As commercial development techniques and tools evolve, so pedagogical methodologies change. Computer science educators have taken different approaches to teaching engineering over the years, both as a result of changing methodologies as well as individual beliefs about what teaching methods work best in a particular academic environment. This paper is a case study in applying current productivity tools (specifically Redmine) in a engineering course at our institution. The objectives of the study are to investigate how to integrate the tools into the existing structure and evaluate their impact. We describe our experience in recently changing our approach to teaching engineering to be more aligned with current tools and how to effectively use them in an academic environment. The paper begins with an overview of engineering teaching methods, then describes our traditional approach and motivation for changing, how the new approach was integrated into the course, our experience with the change, and, finally, our plans for the future Software Engineering Education Background Software Engineering is defined as the application of a systematic, disciplined, quantifiable approach to the development, operation and maintenance of software (Petkovic, Thompson, & Todtenhoefer, 2006, p. 294). The need to teach engineering in colleges has been identified for decades. Stiller & LeBlanc (2002) point out as far back as the early 1990's, ACM Computing Curricula suggested that the at least one engineering course should be required in accredited computer science programs. They point out that the number of large projects in industry demand this and suggest that the proof of the success of these accredited computer science programs should be seen in the reduction of failure in the design and operation of large computer programs. Software engineering programs can be thought of as a replacement for the old apprenticeship programs which taught the trades to workman (Stroulia, Bauer, Craig, Reid, & Wilson, 2011). The issue, however, is how to bring this effect into the classroom. Many educators feel that current practices of teaching engineering are not adequately preparing students for the real world of development. Nurkkala & Brandle (2011) summarize the problems with current teaching approaches: * No product--students are creating projects, not commercial grade products * Short duration--single semester, or two-semester, courses impose an artificial time constraint * High turnover--new students each semester means the talent pool remains shallow and student skills are not developing based on previous experience * Low complexity--by necessity given time constraints and skill sets * No maintenance--as a result of short duration, students do not experience a key aspect of development, the maintenance phase * No customer--most engineering projects do not interface with a real customer To address these shortcomings, different approaches to teaching engineering have emerged and been proposed in the literature. …

Mining people's semantic trajectory behaviours from geotagged photographs