Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 1,980 papers for “Search-based software engineering”

Search-based software engineering

Mark Harman, S. Afshin Mansouri, Yuanyuan Zhang · ACM Computing Surveys · 2012

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.

Cited by 753

Search based software engineering

Micallef, Mark, Computer Science Annual Workshop CSAW’13 · OAR@UM (University of Malta) · 2013

Consider the following questions, which are posed by software engineers on a daily basis: 1. What is the smallest set of test cases that will cover all statements in this program? 2. What is the best way to organise classes and methods for this OO design? 3. What is the set of requirements that balances software development cost and customer satisfaction? Whilst these questions seem to be addressing different problems, they do have some notable commonalities. Firstly, they form part of a large set of soft- ware engineering problems which can each be solved by a multitude of potential solutions. That is to say that if one were to ask the above questions to x equally competent engineers, one would likely get back x different yet correct solutions. Secondly, this class of problems is usually tasked with balancing a number of competing constraints. A typical example here is maximising customer satisfaction whilst keeping development costs low. Finally, whilst there is typically no perfect answer (and indeed no precise rules for computing the best solution), good solutions can be recognised. When problems with similar characteristics were encountered in disciplines other than software engineering, they were solved with a large degree of success using search-based techniques. It was this realisation that gave rise to the field of search based software engineering.

Cited by 0Open access

Search Based Software Engineering

Jiang, H, Tang, K, Petke, J et al. · UCL Discovery (University College London) · 2017

The articles in this special section focus on search-based software engineering. Search Based Software Engineering (SBSE) consists of the application of computational intelligence (CI) algorithms to hard optimization problems in software engineering (SE). It has become an important application field for CI. The term SBSE was coined by Harman and Jones in 2001, although there was work on the application of CI algorithms to SE before this date. After more than fifteen years development, CI algorithms have been used to solve SE tasks in almost all the stages of an SE lifecycle, including requirements, designing, coding, testing and maintenance. solved by three steps.

Cited by 0

Search-Based Software Engineering

Symposium on Search-Based Software Engineering 2024 Porto de Galinhas, Jahangirova, Gunel, Khomh, Foutse · Lecture notes in computer science · 2024

Cited by 6Open access

Search based software engineering

Ali Ouni · 2020

tutorial Share on Search based software engineering: challenges, opportunities and recent applications Author: Ali Ouni University of Quebec University of QuebecView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 1114–1146https://doi.org/10.1145/3377929.3389887Published:08 July 2020Publication History 8citation212DownloadsMetricsTotal Citations8Total Downloads212Last 12 Months35Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Cited by 13

Understanding Search-Based Software Engineering

Paul W. McBurney · 2012

Pareto Optimization is a method of finding the space of best solutions possible given multiple class dimensions. In practice, however, it is difficult for users to visualize how solutions are spaced across this multi-dimensional frontier. Existing algorithms like NSGA-II are capable of quickly finding the Pareto frontier. However, users cannot use these algorithms to understand how changes to a solution effect the qualities of that solution.;In response to these issues, this paper presents "HOW". Not only is "HOW" capable of finding the Pareto frontier across multiple dimensions, but HOW can also explain how each point was derived, and compare points along the frontier to their neighbors. HOW generates rules using a stochastic learner, then combines those rules into a subsumption network via formal concept analysis. The network is augmented with statistics on the training data selected by sub-networks. Users can navigate their decision space by walking that network, selecting regions containing properties they want to avoid or encourage.

Cited by 0Open access

Towards Search-based Game Software Engineering

Daniel Blasco Latorre · 2024

I also want to thank the SVIT Research Group at Universidad San Jorge, an amazing group of people that perfectly embodies what a team should be.

Cited by 0Open access

Data-Driven Search-based Software Engineering

Nair, Vivek, Agrawal, Amritanshu, Chen, Jianfeng et al. · arXiv (Cornell University) · 2018

This paper introduces Data-Driven Search-based Software Engineering (DSE), which combines insights from Mining Software Repositories (MSR) and Search-based Software Engineering (SBSE). While MSR formulates software engineering problems as data mining problems, SBSE reformulates SE problems as optimization problems and use meta-heuristic algorithms to solve them. Both MSR and SBSE share the common goal of providing insights to improve software engineering. The algorithms used in these two areas also have intrinsic relationships. We, therefore, argue that combining these two fields is useful for situations (a) which require learning from a large data source or (b) when optimizers need to know the lay of the land to find better solutions, faster. This paper aims to answer the following three questions: (1) What are the various topics addressed by DSE? (2) What types of data are used by the researchers in this area? (3) What research approaches do researchers use? The paper briefly sets out to act as a practical guide to develop new DSE techniques and also to serve as a teaching resource. This paper also presents a resource (tiny.cc/data-se) for exploring DSE. The resource contains 89 artifacts which are related to DSE, divided into 13 groups such as requirements engineering, software product lines, software processes. All the materials in this repository have been used in recent software engineering papers; i.e., for all this material, there exist baseline results against which researchers can comparatively assess their new ideas.

Cited by 0Open access