Special Issue on Search‐Based Software Maintenance

This special issue of Empirical Software Engineering consists of revised and extended versions of five selected papers originally presented at the 1st International Symposium on Search Based Software Engineering (SSBSE 2009). The symposium was held at Cumberland Lodge, Windsor, UK, in May 2009, and brought together the academic researchers and industrial practitioners that make up the international Search Based Software Engineering (SBSE) community for the first conference dedicated to the subject. Search Based Software Engineering is the application of search-based optimization techniques—for example, genetic algorithms, hill climbing, simulated annealing, taboo search, and ant colony optimization—to the solution of software engineering problems. Many such problems are often difficult or impossible to solve manually, and moreover, as modern software becomes more complex and adopts distributed and highly parallel architectures, are proving to be increasingly intractable using traditional automated techniques. In contrast, SBSE approaches can leverage sophisticated optimisation algorithms running on affordable computing infrastructure to solve these problems. SBSE approaches also demonstrate other advantages: they can be used to find acceptably good solutions when time, or resources, are limited, and—by utilizing multi-objective search algorithms—can derive a range of solutions that illustrate potential trade-offs between competing goals. For these reasons, both academic and industrial interest in SBSE is increasing rapidly. A recent survey,1 undertaken by the UK-based SEBASE project, indicated an exponential growth of

Despite preceding related publications, works dealing with the resolution of software engineering problems by search techniques has especially risen since 2001. By its first decade, the Search Based Software Engineering (SBSE) approach has been successfully employed in several software engineering contexts, using various optimization techniques. Aside the relevance of such applications, knowledge regarding the publication patterns on the field plays an important role to its understanding and identity. Such information may also shed light into SBSE trends and future. This paper presents the first bibliometric analysis to SBSE publications. The study covered 740 publications of the SBSE community from 2001 through 2010. The performed bibliometric analysis concerned mainly in four categories: Publication, Sources, Authorship, and Collaboration. Additionally, estimates for the next years of several publication metrics are given. The study also analyzed the applicability of bibliometric laws in SBSE, such as Bradfords and Lotka.

This challenges and open problems paper discusses the role of software engineers when combining modeling with Search-Based Software Engineering (SBSE). With SBSE techniques increasingly providing insights into difficult software modeling problems, the role of the software engineer might be thought of as diminishing. However, given the crucial role of the model understanding in SBSE, we argue that the role of the software engineer will become more important through interactive SBSE, although exploiting this interaction remains a challenge for the field.

BackgroundRelease planning (RP) is one of the most complex and relevant activities in the iterative and incremental software development, because it addresses all decisions associated with the selection and assignment of requirements to releases. There are many approaches in which RP is formalized as an optimization problem. In this context, search-based software engineering (SBSE) deals with the application of search techniques to solve complex problems of software engineering. Since RP is a wicked problem with a large focus on human intuition, the decision maker’s (DM) opinion is a relevant issue to be considered when solving release planning problem. Thus, we emphasize the importance in gathering the DM’s preferences to guide the optimization process through search space area of his/her interests.MethodsTypically, RP is modelled as a multi-objective problem by considering to maximize overall clients satisfaction and minimize project risk. In this paper, we extend this notion and consider DM’s preferences as an additional objective. The DM defines a set of preferences about the requirements allocation which is stored in a preference base responsible for influencing the search process. The approach was validated through an empirical study, which consists of two different experiments, respectively identified as (a) automatic experiment and (b) participant-based experiment. Basically, the former aims to analyze the approach using different search-based algorithms (NSGA-II, MOCell, IBEA, and SPEA-II), over artificial and real-world instances, whereas the latter aims at evaluating the use of the proposal in a real scenario composed of human evaluations.ResultsThe automatic experiment points out that NSGA-II obtained overall superiority in two of the three datasets investigated, positioning itself as a superior search technique for scenarios with few number of requirements and preferences, while IBEA showed to be better for larger ones (with more requirements and preferences). Regarding the participant-based experiment, it was found that two thirds of the participants evaluated the preference-based solution better than the non-preference-based one.ConclusionsThe results suggest that it is feasible to investigate the approach in a real-world scenario. In addition, we made available a prototype tool in order to incorporate the human’s preferences about the requirements allocation into the solution of release planning.

Many software engineering problems are multi-objective in nature, which has been largely recognized by the Search-based Software Engineering (SBSE) community. In this regard, Pareto- based search algorithms, e.g., Non-dominated Sorting Genetic Algorithm II, have already shown good performance for solving multi-objective optimization problems. These algorithms produce Pareto fronts, where each Pareto front consists of a set of non- dominated solutions. Eventually, a user selects one or more of the solutions from a Pareto front for their specific problems. A key challenge of applying Pareto-based search algorithms is to select appropriate quality indicators, e.g., hypervolume, to assess the quality of Pareto fronts. Based on the results of an extended literature review, we found that the current literature and practice in SBSE lacks a practical guide for selecting quality indicators despite a large number of published SBSE works. In this direction, the paper presents a practical guide for the SBSE community to select quality indicators for assessing Pareto-based search algorithms in different software engineering contexts. The practical guide is derived from the following complementary theoretical and empirical methods: 1) key theoretical foundations of quality indicators; 2) evidence from an extended literature review; and 3) evidence collected from an extensive experiment that was conducted to evaluate eight quality indicators from four different categories with six Pareto-based search algorithms using three real industrial problems from two diverse domains.

In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article 1 provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

Search-Based Software Engineering (SBSE) researchers who apply multi-objective search algorithms (MOSAs) often assess the quality of solutions produced by MOSAs with one or more quality indicators (QIs). However, SBSE lacks evidence providing insights on commonly used QIs, especially about agreements among them and their relations with SBSE problems and applied MOSAs. Such evidence about QIs agreements is essential to understand relationships among QIs, identify redundant QIs, and consequently devise guidelines for SBSE researchers to select appropriate QIs for their specific contexts. To this end, we conducted an extensive empirical evaluation to provide insights on commonly used QIs in the context of SBSE, by studying agreements among QIs with and without considering differences of SBSE problems and MOSAs. In addition, by defining a systematic process based on three common ways of comparing MOSAs in SBSE, we present additional observations that were automatically produced based on the results of our empirical evaluation. These observations can be used by SBSE researchers to gain a better understanding of the commonly used QIs in SBSE, in particular, regarding their agreements. Finally, based on the results, we also provide a set of guidelines for SBSE researchers to select appropriate QIs for their particular context.

With the aim of identifying good structures and examples for papers in the software engineering field, we conducted a study of the type of papers accepted along four decades in the Research Track of the International Conference on Software Engineering (ICSE). We used for this purpose a categorization scheme for Software Engineering papers that was obtained by merging, extending and revising a few existing paper scheme proposals. This paper summarizes some outcomes relative to what topics and problems are addressed, what types of contribution are presented and how they are validated. Insights from the study could help ICSE authors, reviewers and conference organizers in focusing and improving future efforts.

Search-Based Software Engineering (SBSE) tools offer automatic support by applying search-based techniques to solve different optimization problems of various software engineering areas. However, configuring SBSE tools often requires expertise in optimization techniques, posing a significant barrier for non-specialist users. This task involves selecting suitable algorithms, tuning parameters, choosing appropriate objective functions and search operators, and making decisions that affect optimization quality and performance. To address this challenge, we propose a multiple-LLM approach that integrates Large Language Models (LLMs), specifically ChatGPT-4, into SBSEworkflows to provide context-aware and natural language suggestions for parameter configuration. The approach follows a Divide and Conquer strategy, in which specialized LLMs are trained for distinct parameter configuration subproblems. As a proof of concept, we implemented OPLA-Wizard that works with OPLA-Tool v2.0, for Product Line Architecture (PLA) design. This assistant supports users in configuring algorithm settings, search operators, and objective functions through a user-friendly interface. Preliminary results show that the multiple-LLM approach improves accuracy, explanation clarity, and output format adherence compared to a single-LLM approach. This work contributes to the democratization of SBSE by reducing cognitive load, preventing misconfigurations, and making advanced optimization techniques more accessible to a broader audience.

This survey reviews published materials related to the specific area of Search-Based Software Engineering that concerns software maintenance and, in particular, refactoring. The survey aims to give a comprehensive review of the use of search-based refactoring to maintain software. Fifty different papers have been selected from online databases to analyze and review the use of search-based refactoring in software engineering. The current state of the research is analyzed and patterns in the studies are investigated in order to assess gaps in the area and suggest opportunities for future research. The papers reviewed are tabulated in order to aid researchers in quickly referencing studies. The literature addresses different methods using search-based refactoring for software maintenance, as well as studies that investigate the optimization process and discuss components of the search. There are studies that analyze different software metrics, experiment with multi-objective techniques and propose refactoring tools for use. Analysis of the literature has indicated some opportunities for future research in the area. More experimentation of the techniques in an industrial environment and feedback from software developers is needed to support the approaches. Also, recent work with multi-objective techniques has shown that there are exciting possibilities for future research using these techniques with refactoring. This survey is beneficial as an introduction for any researchers aiming to work in the area of Search-Based Software Engineering with respect to software maintenance and will allow them to gain an understanding of the current landscape of the research and the insights gathered.

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.

Software engineering, in applying engineering to software, advocates the application of systematic, disciplined, quantifiable approaches to development, operation, and maintenance of software. Software engineering is also considered a transformational process that models the real world on a corresponding software world. Recent advances in software engineering applied to Web intelligence have been recognized as a wave in scientific research and development, exploring basic roles and practical impact of advanced information technology on next-generation Web-empowered products, systems, and services. Various web systems and services currently provide many benefits to users, with Web intelligence becoming increasingly important in research and business. Such Web intelligent systems have been realized through related technologies as software engineering, interactive machine learning, etc. This special issue brings together researchers who have inspired software engineering in Web intelligence. Submissions have been reviewed for relevance, originality, significance, and presentation based on JACIII review criteria. This special issue presents four papers describing outstanding studies on project management, service-oriented architecture, semantic Web technologies, and service composition planning in Web intelligence. All papers introduce promising approaches and interesting results that readers will find highly relevant! We believe that software engineering in Web Intelligence has tremendous potential as a new, active research field, and we hope this issue will motivate researchers to expand their own studies on software engineering in Web Intelligence.

<p>In the current technological scenario of the industry and businesses, there has been increasing need of software within systems and also an increasing demand being put onto software-intensive systems. This in effect will lead to a significant evolution of software engineering processes over the next twenty years. This is due to the fact of emerging technological advancements like Industry 4.0 and Internet of Things in the IT field, among other new developments. This paper addresses and tries to analyses the key research challenges being faced by the software engineering field and articulates information that is derived from the key research specializations within software engineering. The paper analyses the past and current trends in software engineering. The future of software engineering is also looked with respect to Industry 4.0 which including emerging technological platforms like Internet of Things. The societal impact aspect of future trends in software engineering is also addressed in this paper.</p>

We welcome you to the Twenty-Eighth International Conference on Software Engineering and Shanghai, China. This is the first instance of the ICSE conference series to be held in China, and it is an exciting time and place for ICSE 2006. The conference brings researchers, educators, practitioners and students together to present, exchange ideas and engage in discussions about the recent and exciting innovations and research in software engineering. Exciting sessions on practical experience, educational issues, and doctoral and new faculty symposia have been organized. The conference also includes a wide variety of tutorial, workshops and demonstration offerings. Finally, since research and markets in our host country China are continuously growing, ICSE includes a separate Far East Track for discussions on the contributions and challenges of our Chinese colleagues. We trust that this mix will provide interesting and stimulating offerings to all members of our growing community. The technical program committee is pleased to present this conference proceeding that contains the research papers of the technical program for ICSE 2006. The technical program for this conference focuses on new and innovative software engineering contributions in theory, formal methods, system building and empirical studies. The papers represent an alluring array of topics, including software design, architecture, reuse, testing, analysis, processes and formal methods. Because of the growing number of paper submission and the resulting large program committees in the past, we instituted a new two-phase reviewing process this year. Our program committee had 36 members. In the first phase, each submitted paper was reviewed by 3 reviewers: 2 from the program committee and an external reviewer from the software engineering community. We had 265 external reviewers who contributed 370 reviews for this phase. Those papers receiving the best reviews were considered in the second phase. A total of 99 papers entered the second phase. Each of these papers was reviewed by another program committee member. Thus, each accepted paper had a total of 4 reviews (3 reviews from pc members and 1 review from an external reviewer). Each rejected paper had at least 3 reviews. The pc members reviewed 24-25 papers in the two phases, a reasonable number. We feel that this system worked very well. The program committee met for two days in December, 2005 in Frankfurt, Germany. By the end of the meeting, a consensus emerged for the 36 papers in this proceedings (9% of the total submissions). There were many high quality submissions that could not be accommodated in the program; hopefully a revised version will be published elsewhere. In addition to the papers, we organized four invited sessions. One invited session focuses on empirical studies, an increasingly important component of software engineering research and practice. We also have an invited panel that addresses the value and practice of formal methods. Since software engineering techniques are growing in importance in challenging safety critical domains, we included two invited sessions: one on software engineering for automotive engineering and the other on software engineering for assisted living. We are very pleased that a notably diverse and outstanding group of three leaders of the software engineering community have agreed to present keynote speeches. The opening day keynote will be presented by Prof. Yang Fuqing, from Peking University, a woman who has been at the very forefront of the software engineering communities of research and practice in China for decades. Her perspectives on the past and future of software engineering in China will be complemented by a keynote on similar themes presented on the second day. This second keynote will be delivered by Prof. Barry Boehm, from the University of Southern California, USA. His talk will review the history of software engineering research and practice in the West over the past decades, and project it forward into the 21st century. Finally, the keynote speaker on the third day, Mr. Reinhold Achatz, from Siemens Corp., will present a complementary view of software engineering research and practice, as viewed from the perspective of one of the great multinational corporations, based in Europe. We are truly excited at the prospect that these three keynotes will provide a searching multidimensional view of our community at this time of historic convergence of communities. The continued success and high quality of the ICSE conference series would not be possible without the help and support of the software engineering community. First, we would like to gratefully acknowledge and thank all of the authors who submitted papers. We especially thank the program committee members for their efforts and commitments in reviewing the papers and securing reviews from external reviewers. They put in an extraordinary amount of their time to provide thoughtful and knowledgeable reviews. The exceptionally strong technical program represented by the papers in this proceeding could not be possible without their efforts.

Software is an engineering material to be optimised. Until comparatively recently many computer scientists doubted this; why would one want to optimise something that could be made perfect by pure logical reasoning? However, the wider community has come to realise that, while very small programs may be perfect in isolation, larger software systems may never be (because the world in which they operate is not perfect). Once we accept this, we soon arrive at evolutionary computation as a means of optimising software. However, software is not merely some other engineering material to be optimised. Software is virtual and inherently adaptive, making it better suited to evolutionary computation than any other engineering material. This is leading to breakthroughs at the interface of software engineering and evolutionary computation, though there are still many exciting open problems for evolutionary commutation researchers to get their teeth into. This talk will cover some of these recent developments in Search Based Software Engineering (SBSE) and Dynamic Adaptive SBSE.