On the problem of the software cost function

Dolado, José Javier

doi:10.1016/s0950-5849(00)00137-3

Cited by 141 publications

(79 citation statements)

References 20 publications

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“…(Recent research, using genetic algorithms as a flexible method of model fitting, did not find any significant deviations from a linear model [16]. )…”

Section: Model Specificationmentioning

confidence: 99%

A simulation study of the model evaluation criterion mmre

Foss

Stensrud²,

Kitchenham

et al. 2003

IIEEE Trans. Software Eng.

380

275

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“…(Recent research, using genetic algorithms as a flexible method of model fitting, did not find any significant deviations from a linear model [16]. )…”

Section: Model Specificationmentioning

confidence: 99%

A simulation study of the model evaluation criterion mmre

Foss

Stensrud²,

Kitchenham

et al. 2003

IIEEE Trans. Software Eng.

380

275

View full text Add to dashboard Cite

show abstract

“…Nevertheless, as a result of the high complexity of this activity, the search for efficient and effective estimation models is still underway. An interesting example on the application of a search based approach -genetic programming, in this case -to tackle the software estimation problem can be found in [25]. In this application, the software estimation problem is modeled as a search problem, considering as search space the set of cost predictive functions which will have their predictive capability evaluated based on some particular measure.…”

Section: Case Study: Cost Estimation For Project Planningmentioning

confidence: 99%

Search Based Software Engineering: Techniques, Taxonomy, Tutorial

Harman

McMinn

Souza

et al. 2012

Empirical Software Engineering and Verification

224

257

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Abstract. 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. IntroductionSearch Based Software Engineering (SBSE) is the name given to a body of work in which Search Based Optimisation is applied to Software Engineering. This approach to Software Engineering has proved to be very successful and generic. It has been a subfield of software engineering for ten years [45], the past five of which have been characterised by an explosion of interest and activity [48]. New application areas within Software Engineering continue to emerge and a body of empirical evidence has now accrued that demonstrates that the search based approach is definitely here to stay. SBSE seeks to reformulate Software Engineering problems as 'search problems' [45,48]. This is not to be confused with textual or hypertextual searching. Rather, for Search Based Software Engineering, a search problem is one in which optimal or near optimal solutions are sought in a search space of candidate solutions, guided by a fitness function that distinguishes between better and worse solutions. The term SBSE was coined by Harman and Jones [45] in 2001, which was the first paper to advocate Search Based Optimisation as a general approach to Software Engineering, though there were other authors who had previously applied search based optimisation to aspects of Software Engineering.SBSE has been applied to many fields within the general area of Software Engineering, some of which are already sufficiently mature to warrant their own surveys. For example, there are surveys and overviews, covering SBSE for requirements [111], design [78] and testing [3,4,65], as well as general surveys of the whole field of SBSE [21,36,48]. This paper does not seek to duplicate these surveys, though some material is repeated from them (with permission), where it is relevant and appropriate. Rather, this paper aims to provide those unfamiliar with SBSE with a tutorial and practical guide. The aim is that, having read this paper, the reader will be able to begin to develop SBSE solutions to a chosen software engineering problem and will be able to collect and analyse the results of the applicati...

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“…Also, to the authors' knowledge the evaluation previous work on these problems has been based solely on synthetic rather than real data. For the related topic of search based cost estimation for software projects [29,30,31], real data is routinely used in evaluation, but for SBSPP there is comparatively little real world data available. Synthetic data is very useful for experimenting with search based algorithms under controlled conditions; it facilitates exploration of scalability and robustness.…”

Section: Related Workmentioning

confidence: 99%

The use of search‐based optimization techniques to schedule and staff software projects: an approach and an empirical study

2011

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SUMMARYAllocating resources to a software project and assigning tasks to teams constitute crucial activities that affect project cost and completion time. Finding a solution for such a problem is NP-hard; this requires managers to be supported by proper tools in performing such an allocation. This paper shows how search-based optimization techniques can be combined with a queuing simulation model to address these problems. The obtained staff and task allocations aim to minimize the completion time and reduce schedule fragmentation. The proposed approach allows project managers to run multiple simulations, compare results and consider trade-offs between increasing the staffing level and anticipating the project completion date and between reducing the fragmentation and accepting project delays. The paper presents results from the application of the proposed search-based project planning approach to data obtained from two large scale commercial software maintenance projects.

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On the problem of the software cost function

Cited by 141 publications

References 20 publications

A simulation study of the model evaluation criterion mmre

A simulation study of the model evaluation criterion mmre

Search Based Software Engineering: Techniques, Taxonomy, Tutorial

The use of search‐based optimization techniques to schedule and staff software projects: an approach and an empirical study

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