Special Issue on Software Engineering for Web Intelligence

Search-based software engineering (SBSE) consists of the application of metaheuristic search techniques—such as genetic algorithms, simulated annealing, hill climbing, and tabu search—to software engineering problems. This idea is based on the observation that some software engineering activities can be thought of as optimization problems, and on the fact that techniques providing exact solutions are, very often, unable to adequately scale to large, real-world software engineering problems. Define fitness functions to guide an automated search for solutions, rather than attempting to construct solutions from scratch by hand. SBSE moves the engineer up the abstraction chain, from the tedious and expensive role of detailed solution construction, to the potentially more productive and more cost-effective area of fitness function design. This approach has been repeatedly demonstrated to have successful applications across the software engineering life cycle from requirements and project planning to maintenance and testing. This special issue focuses on applications of search SBSE to the maintenance phase of the life cycle. After software testing 2, software maintenance is the most widely studied application area for SBSE research. Work on SBSE applications to problems in software maintenance can be traced back to the pioneering work of the Mancoridis et al. 3, which led to the development of the Bunch tool for software re-modularization 4. In the past five years there has been a significant increase in SBSE applications to software maintenance and evolution. Search-based techniques have been applied to post-development code improvement problems, such as object-oriented refactoring 5, 6, and imperative language slicing and transformation 7. Other applications are related to software engineering economics and project management problems, such as release planning 8-12, effort, and cost estimation 13-16, and project staffing and planning 17-20. Last, but not the least, SBSE has been applied to deal with service-oriented architectures, for example for services QoS-aware composition 21, service-level agreement negotiation 22, and testing 23. A complete overview of SBSE with a discussion of future research trends can be found in the recent International Conference on Software Engineering (ICSE) ‘Future of Software Engineering’ paper 24. This issue of the Journal of Software Maintenance and Evolution: Research and Practice features three outstanding papers, dealing with different aspects of software maintenance and evolution: release planning and staffing, re-factoring, and component substitution. The paper ‘Optimized Staffing for Product Releases—Focused Search and its Application at Chartwell Technologies’ by Puneet Kapur, An Ngo-The, Günther Ruhe, and Andrew Smith presents an approach for optimizing the staffing for product releases. The problem they solve can be viewed as part of the more general release planning problem, which assigns features to releases such that technical, resource, risk, and budget constraints are met. In particular, the genetic search-based approach the authors propose aims at assigning human resources to software development tasks, taking them from a pool of developers having different levels of skills. The applicability of the proposed approach is demonstrated with an industrial case study. The paper ‘Search-Based Refactoring: an Empirical Study’ by Mark O'Keffe and Mel Ó Cinnéide presents an approach to automatically re-factor, using search-based techniques, object-oriented systems that undergo repeated addition of functionality. Such addition can, very often, deteriorate the quality of the system's underlying design. Their re-factoring is guided by the QMOOD quality model 25, and the paper compares different optimization techniques, such as Genetic Algorithms, Simulated Annealing, and Multiple Ascent Hill-Climbing. The results of experiments carried out on five software systems suggest that the latter technique outperforms the others. The paper ‘Search-based Many-to-one Component Substitution’ by Nicolas Desnos, Marianne Huchard, Guy Tremblay, Christelle Urtado, and Sylvain Vauttier presents a search-based approach that allows the replacement of an obsolete component with a composition of multiple components. When a component becomes obsolete, the approach searches for a minimal assembly of other components, exposing their functionality by means of ports that preserves the behavior of the original component. The proposed approach has been evaluated on a set of randomly generated components. We hope that readers will enjoy this special issue and find the selected papers insightful and inspiring. We would like to thank all the authors who presented papers for this special issue: there were many good quality papers, but unfortunately we could accept only three of them. Last, but not the least, we would like to thank the reviewers who provided timely, detailed and constructive feedbacks to the authors. Without their precious help this special issue would not have been possible.

With the enhancement of hardware and software engineering, the effectiveness and correctness of software is less and less doubted and customers are more aware about whether software services are available or not when needed. Software maintenance is one of the main reasons that make software unavailable and it is often very expensive to perform maintenance tasks. Common approaches of studying software maintenance are to consider it as a static by‐product of software operation and only the maintenance cost is covered. In this paper, software maintenance policies are studied with the consideration of unavailable service time. A non‐homogeneous continuous Markov chain is adopted for modeling the software operation and maintenance process, and the cost of software unavailability that is brought in by software maintenance is investigated and analyzed for searching the optimal maintenance policy, which aims at minimizing the average maintenance time cost. The optimality of our proposed policy is shown and checked by numerical examples with discussions of its possible application perspectives. Copyright © 2010 John Wiley & Sons, Ltd.

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. …

Abstract. The rapid advancement of Artificial Intelligence (AI) has significantly influenced various industries, including software engineering. This paper explores the integration of AI into software engineering, focusing on its applications across different stages of the software development life cycle, including design, development, testing, project management, and maintenance. AI's ability to automate tasks, enhance efficiency, and improve code quality is revolutionizing how software is built and maintained. The paper also addresses the challenges and risks associated with AI-driven software engineering, such as dependency on AI tools, ethical concerns, and security vulnerabilities. Finally, the paper highlights future trends in AI-powered software engineering, including adaptive and self-healing systems, AI-enhanced collaboration, and full software automation. The role of AI in shaping the future of software engineering is both profound and transformative, making it a critical area of study.

During the "The National Forum on the Future of the Defense Health Information System," a track focusing on "Systems Architecture and Software Engineering" included eight presenters. These presenters identified three key areas of interest in this field, which include the need for open enterprise architecture and a federated database design, net centrality based on service-oriented architecture, and the need for focus on software usability and reusability. The eight panelists provided recommendations related to the suitability of service-oriented architecture and the enabling technologies of grid computing and Web 2.0 for building health services research centers and federated data warehouses to facilitate large-scale collaborative health care and research. Finally, they discussed the need to leverage industry best practices for software engineering to facilitate rapid software development, testing, and deployment.

The Software Engineering Institute's Softwares Process Maturity Model was developed to provide a basis for assessing an organization's ability to perform software engineering activities. It also provides a guide for improving an organization's software engineering capability. The maturity model was designed on the basis of concepts associated with developing new, large, complex systems. This has led to difficulties in applying the model to organizations involved entirely in software maintenance. The author addresses the tailoring issues encountered in applying the software maturity model to a large maintenance organization at Paramax Space Systems Operation. It is concluded that the issues that arise when the Capability Maturity Model is applied to a software sustaining engineering organization are significant but do not prohibit the use of the model as a guide for software process improvement. Issues mainly arise with the treatment requirements and differences in project management needs. The primary tailoring issues are ones of wording as opposed to applicability to maintenance activities. >

Software is being produced so fast that its growth hinders its sustainability. Technical debt, which encompasses internal software quality, evolution and maintenance, reengineering, and economics, is growing such that its management is becoming the dominant driver of software engineering progress. It spans the software engineering life cycle, and its management capitalizes on recent advances in fields such as source code analysis, quality measurement, and project management. Managing technical debt will become an investment activity applying economic theories. It will effectively address the architecture level and will offer specific processes and tools employing data science and analytics to support decision making. It will also be an essential part of the software engineering curriculum. Getting ahead of the software quality and innovation curve will inevitably involve establishing technical-debt management as a core software engineering practice. This article is part of a special issue on the Future of Software Engineering.

Motivation in software engineering is a complex topic. Cultural background is reported to be one of the factors moderating software engineers' motivation and project outcome. The authors conducted a survey with 36 software engineers from Finland to explore 1) the relationship between team motivation and project outcome, 2) factors that motivate Finnish engineers, and 3) how these motivational factors are related. The authors compare Finnish motivational factors with those identified in prior research. In addition they build a prediction model to identify the best indicators of team motivation for Finnish software engineers. Their results show that teamwork is the only culturally independent motivational factor. Having 1) a project manager with a clear vision (project manager vision) and 2) a project manager given full authority to manage the project (project manager authority) are also significant motivational factors among Finnish engineers. There are significant associations between some factors, e.g., customer involvement and staff appreciation. While these factors partially explain motivation in software engineering, cultural differences also play an active role in explaining team motivation. Their questionnaire needs to be updated to enable measurement of motivation for modern development practices such as agile development.

This chapter addresses the application of computer games and simulations in order to explore reality in many educational areas. The Games-Based Learning (GBL) can improve the teaching and learning experience by training future professionals in real life scenarios and activities that enable them to apply problem-solving strategies by putting into use the correct technique stemming from their own skills. For that reason, GBL has been used in software engineering teaching. At Pontifical Catholic University of Rio de Janeiro, the authors have developed SimulES-W (Simulation in Software Engineering), a tool for teaching software engineering. SimulES-W is a collaborative software board game that simulates a software engineering process in which the player performs different roles such as software engineer, technical coordinator, project manager, and quality controller. The players can deal with budget, software engineer employment and dismissal, and construction of different software artifacts. The objective of this chapter is to describe the approach to teaching software engineering using SimulES-W and demonstrate how pedagogical methodology is applied in this teaching approach to improve software engineering education. The teaching experience and future improvements are also discussed.

Welcome to the Empirical Software Engineering’s special issue on predictive models in software engineering. The goal of such methods is repeatable, refutable (and possibly improvable) results in software engineering. Many of the recent papers in SE literature are based on data from on-line repositories such as http://promisedata.googlecode.com. This introduces a kind of selection in the kinds of papers published at this venue. Our first paper pushes past that bias to explore a very rich time-based data set. In “Predicting the Flow of Defect Correction Effort using a Bayesian Network Model”, Schulz et al. use a Bayes net to explore the effects of removing defects at different stages of the software lifecycle. Their work shows how to calibrate general models to the particulars of a company’s local particulars. Our next paper “The Limited Impact of Individual Developer Data on Software Defect Prediction” by Bell et concludes there is no added value to reasoning on some aspects of social aspects of programmer teams working on a code. This is a timely counterpoint to other research that eschews code measures for other approaches based only on social metrics. Our last paper explores the complicated issue of parameter tuning. In “Using Tabu Search to Configure Support Vector Regression for Effort Estimation”, Corazza et al. offers automated guidance for setting the parameters that control a learner. This is a matter of critical importance since even the best learner can perform poorly if its operator uses the wrong settings. A special issue like this is only possible due to the hard work of a dedicated set of authors are reviewers. We would like to express our gratitude to all authors who submitted their papers this special issue. We would also like to thank our reviewers for their meticulous evaluation of the submissions. The success of special issues such as this one largely stands on their shoulders. Empir Software Eng (2013) 18:433–434 DOI 10.1007/s10664-013-9252-1

This special issue focuses on issues arising from the RAISE’12 Workshop on Realizing Artificial Intelligence Synergies in Software Engineering. Our objective is to provide a forum for researchers and industrial practitioners to exchange and discuss the latest innovative synergistic AI and SE techniques/practices. Why explore this combination of SE and AI? We think there are many answers to that question. As SE is asked to answer dynamic automated, adaptive, and/or large-scale demands, other computer science disciplines come to play. AI is one of them that may bring SE to further heights. Conversely, SE can also play a role to alleviate development costs and the development effort associated with AI tools. Such mutually beneficial characteristics have appeared in the past few decades and still evolve due to new challenges. That is, this special issue explores not only the application of AI techniques to software engineering problems but also the application of software engineering techniques to AI problems. This special issue is the result of much work that is still ongoing. As to future work, the RAISE series is also on-going. At the time of this writing, the RAISE’13 workshop has just completed (that event was sponsored by the United States National Science Foundation, and we thank them for their generous support). At that meeting, we saw much continued interest in this union of AI and SE. The reader should expect much novel and exciting work from this combination of ideas, in the very near future. And regarding the work seen to date, we hope that all the aforementioned papers will provide readers with some glimpse of the kind of work discussed at RAISE’12. Also, we would like to sincerely thank the reviewers for their assistance in the reviewing process.

The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The amount of information grows billions of databases. We need to search the information will specialize tools known generically search engine. There are many of search engines available today, retrieving meaningful information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper we present survey on the search engine generations and the role of search engines in intelligent web and semantic search technologies.

Empathy, defined as the ability to understand and share others’ perspectives and emotions, is essential in software engineering (SE), where developers often collaborate with diverse stakeholders. It is also considered as a vital competency in many professional fields such as medicine, healthcare, nursing, animal science, education, marketing, and project management. Despite its importance, empathy remains under-researched in SE. To further explore this, we conducted a socio-technical grounded theory (STGT) study through in-depth semi-structured interviews with 22 software developers and stakeholders. Our study explored the role of empathy in SE and how SE activities and processes can be improved by considering empathy. Through applying the systematic steps of STGT data analysis and theory development, we developed a theory that explains the role of empathy in SE. Our theory details the contexts in which empathy arises, the conditions that shape it, the causes and consequences of its presence and absence. We also identified contingencies for enhancing empathy or overcoming barriers to its expression. Our findings provide practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

Most graduates of baccalaureate computing programs (at least in the United States) take jobs in the computing profession immediately upon graduation. These jobs generally involve software development or maintenance activities, and therefore demand certain software engineering skills and knowledge.It appears, however, that typical undergraduate computing programs are at present inadequately preparing their students in the area of software engineering. This was in evidence at the 1991 International Conference on Software Engineering, in comments made at plenary sessions and in a special workshop on directions in software engineering education. It is also evidenced by recent attempts to encourage development of software engineering programs at the undergraduate level, separate from those in computer science or computer engineering.The purpose of this panel is to explore the following question: “What software engineering knowledge and skills should be required in a typical baccalaureate computing program, assuming the program is preparing its graduates for immediate entry into the computing profession?” In addressing this question, the panel will consider the recent report of the ACM/IEEE-CS Joint Curriculum Task Force, proposed software engineering curricula for undergraduates, and accreditation criteria.

Over time much has been written on how to organize a course on software engineering, with arguments flowing for and against additional complexity. It is highly believe that SWE is an umbrella course in computing and most dangerously in any course proclaimed to be specialist in generating Software Engineers. Unfortunately, this may still not be widely recognized, both in the industry and in academia. The modern course on SWE requires that the student is familiar not just with many aspects of computing such as programming languages but rather with many other aspects including networking, Internet technologies, programming languages, databases, Human Computer Interaction (HCI), computer graphics, and project management. Depending on the year of academic study these expectations could be unrealistic, especially if that is a 2nd year course. Based on the above view, it is essential to investigate what are the necessary attributes of an ideal Software Engineering at the undergraduate level and how much it is covering of the requirements imposed by the business and industry market. This paper will investigate how appropriate are Software Engineering courses at the undergraduate level in terms of the areas of concentration, delivery approach, and the real life practical training received by the target group of students. It will deeply investigate the overall intended learning objectives and how they are related to the choice of modules offered. On the other hand, a number of business institutions will be surveyed for achieving an overview of how effective graduates of Software engineering courses are within these selected institutions and most importantly what are the challenges they encounter in professional life given their level of university education i.e. how helpful their undergraduate education has been helpful to their development as specialists.