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 article1 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.

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Search-based software engineering: Trends, techniques and applications

In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to software engineering in which search based optimisation algorithms are used to address problems in Software Engineering. SBSE has been applied to problems throughout the Software Engineering lifecycle, from requirements and project planning to maintenance and re-engineering. The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This paper provides a review and classification of literature on SBSE. The paper 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.

/pdf/search-based-software-engineering-a-comprehensive-analysis-h98ne3mmop.pdf

Search Based Software Engineering: A Comprehensive Analysis and Review of Trends Techniques and Applications

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

The aim of Search Based Software Engineering (SBSE) research is to move software engineering problems from human-based search to machine-based search, using a variety of techniques from the metaheuristic search, operations research and evolutionary computation paradigms. The idea is to exploit humans' creativity and machines' tenacity and reliability, rather than requiring humans to perform the more tedious, error prone and thereby costly aspects of the engineering process. SBSE can also provide insights and decision support. This tutorial will present the reader with a step-by-step guide to the application of SBSE techniques to Software Engineering. It assumes neither previous knowledge nor experience with Search Based Optimisation. The intention is that the tutorial will cover sufficient material to allow the reader to become productive in successfully applying search based optimisation to a chosen Software Engineering problem of interest.

Search Based Software Engineering: Techniques, Taxonomy, Tutorial.

https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1006&context=electricalpub

Towards an early software estimation using log-linear regression and a multilayer perceptron model

Algorithmic effort prediction models are limited by their inability to cope with uncertainties and imprecision present in software projects early in the development life cycle. In this paper, we present an adaptive fuzzy logic framework for software effort prediction. The training and adaptation algorithms implemented in the framework tolerates imprecision, explains prediction rationale through rules, incorporates experts knowledge, offers transparency in the prediction system, and could adapt to new environments as new data becomes available. Our validation experiment was carried out on artificial datasets as well as the COCOMO public database. We also present an experimental validation of the training procedure employed in the framework.

Adaptive fuzzy logic-based framework for software development effort prediction

The release planning (RP) problem can be investigated from two dimensions -- what to release and when to release. We investigate the "what" to release decision in terms of which new features or change requests should be assigned and implemented in which releases of a software system. RP for evolving systems is challenging, because the new features might require changes to the existing system. A major drawback of existing RP methods is that, they do not consider the existing systems in making RP decisions. In this paper, we present a technique to detect coupling between features based on relatedness of the components that would implement the features. The components implementing the features are derived from change impact analysis. We integrate the results from feature coupling into a RP strategy that encourages the assignment of highly coupled features in the same release. This helps to avoid haphazard implementation of related features. We present a decision support approach that formulates the RP problem as a bi-objective optimization problem. Our Bi-Objective Release Planning for Evolving Systems (BORPES) is aimed at optimizing the value of release plans from both the business perspectives and the implementation perspectives. This paper presents BORPES in detail and reports on a proof-of-concept case study that investigates the applicability of the proposed approach. The bi-objective optimization offers a set of Pareto-optimal solutions.

Bi-objective release planning for evolving software systems

Algorithmic effort prediction models are limited by their inability to cope with imprecision present in software projects early in the development life cycle. A critical survey carried out by Satin and Ahmed [17] reveals the lack of adaptive soft computing based effort prediction systems that provide complete transparency to the prediction system building strategies. In addition, efforts to model the imprecision problem in one of the most widely used algorithmic model, COCOMO, have not been appropriate [17]. The components of COCOMO model were addressed independently. Integrating the individual component into a single prediction system remains an open question. In this paper, we present a transparent and adaptive fuzzy logic framework for effort prediction based on COCOMO. The training strategies we have implemented in the framework tolerates imprecision, explains prediction rationale through rules, incorporates experts knowledge, and could adapt to a new environment as new data becomes available. Validation of the framework potentials has been carried out on artificial datasets and the COCOMO public database of completed projects.

http://ieeexplore.ieee.org/iel5/9281/29474/01336241.pdf

Towards adaptive soft computing based software effort prediction

Incremental software development is becoming an important tendency in software engineering. A software release is a collection of new and/or changed features that form a new product. Release planning for incremental software development assigns features to sequence of releases in the most beneficial way within the resources available. Release planning is a complex problem where most of the data available are usually uncertain. In this paper, we propose an approach that improves on existing methods for release planning by handling the uncertainty of data using fuzzy logic. We use fuzzy logic to model the uncertainty concerning the identification of structural dependency constraints between requirements. All the concepts and the complete approach are illustrated by a case study example

Fuzzy Structural Dependency Constraints in Software Release Planning

Software development effort prediction is one of the most critical activities in managing software projects. Algorithmic effort prediction models, which have dominated the software engineering community, are limited by their inability to cope with uncertainties and imprecision present in software projects early in the development life cycle. More recently, attention has turned to a variety of machine learning methods, and soft computing in particular to predict software development effort. Soft computing is a consortium of methodologies centering on fuzzy logic (FL), artificial neural networks (ANN), and evolutionary computation (EC). There are evidences that soft computing has been able to address some of the problems associated with previous models. However, there is no clear common ground (criteria) for assessing, classifying, and comparing these different soft computing based effort prediction techniques. In this chapter, we present an evaluation scheme for assessing and comparing soft computing based effort prediction techniques. We present a critical survey of the state-of-the-art application of soft computing in development effort prediction using the set of attributes proposed. Results from our survey reveal that many openings exist for improving soft computing based prediction techniques.

Moshood Omolade Saliu

Papers

Adaptive fuzzy logic-based framework for software development effort prediction

Bi-objective release planning for evolving software systems

Towards adaptive soft computing based software effort prediction

Fuzzy Structural Dependency Constraints in Software Release Planning

Soft Computing Based Effort Prediction Systems — A Survey