Negative results for software effort estimation

Menzies, Tim; Yang, Ye; Mathew, George; Boehm, Barry; Hihn, Jairus

doi:10.1007/s10664-016-9472-2

Cited by 67 publications

(64 citation statements)

References 63 publications

(74 reference statements)

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“…The insufficiency of COCOMO 81 to meet all the needs of these rapid advancements led the origin of COCOMO II . Boehm's COCOMO model is considered as an important milestone for all the SDEE models, and it is also being currently practiced . COCOMO II takes the size of a project in terms of K S L O C , which can be computed directly if the software has been developed or it can be approximated from FPs by using UFP to SLOC conversion table .…”

Section: Overview Of Selected Studiesmentioning

confidence: 99%

“…76,89 Boehm's COCOMO model is considered as an important milestone for all the SDEE models, and it is also being currently practiced. 20,90 COCOMO II takes the size of a project in terms of KSLOC, which can be computed directly if the software has been developed or it can be approximated from FPs by using UFP to SLOC conversion table. 33,91,92 In COCOMO II post-architectural model (6), the scale factors (SFs) have exponential effect, while effort multipliers (EMs) have linear effect on effort value.…”

Section: Overview Of Selected Studiesmentioning

confidence: 99%

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The state‐of‐the‐art in software development effort estimation

Gautam

Singh

2018

J Software Evolu Process

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The software developers and researchers have been facing difficulties regarding software development effort estimation (SDEE) since 1960s. Both overestimation and underestimation are problematic for future software development. The software engineering field is continuously adapting new technologies and development methodologies, so there is always a requirement to have an accurate SDEE method that can cater the needs of continually growing software industry. The major purpose of this state‐of‐the‐art review is to provide an additional insight of existing SDEE studies while considering five points of reference: techniques used to construct models, strengths and weaknesses of different models, availability of benchmark data sets, data set characteristics, generalization ability of models. We have performed a comprehensive review of SDEE studies published in the period 1981‐2016. We have defined a new scheme of categorizing existing SDEE models. We have found that a majority of available data sets do not include complete information of projects, which misleads the direction of research. To compare SDEE models, we recommend to use same data sets while focusing on specific aspects of accuracy as none of SDEE studies has yet been able to compare all the existing models over same data sets while considering same aspects of accuracy.

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Section: Overview Of Selected Studiesmentioning

confidence: 99%

Section: Overview Of Selected Studiesmentioning

confidence: 99%

The state‐of‐the‐art in software development effort estimation

Gautam

Singh

2018

J Software Evolu Process

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“…Regardless of the chosen approach (theoretical vs empirical or a combination), the type of data used (single vs cross company, in‐house vs public, etc), or the techniques applied to produce scientific findings (statistics, machine learning, etc), these studies can produce valuable results, but it is necessary that they use proven/reliable evaluation techniques …”

Section: Related Researchmentioning

confidence: 99%

“…39,40 Regardless of the chosen approach (theoretical vs empirical or a combination), 41 the type of data used (single vs cross company, in-house vs public, etc), 9,42 or the techniques applied to produce scientific findings (statistics, machine learning, etc), 26,43 these studies can produce valuable results, but it is necessary that they use proven/reliable evaluation techniques. 31,44 The model presented in this study represents an approach to the prediction problem that is similar to that used by human estimators. Therefore, it is most appropriate to support the process of expert effort estimation or a so-called judgment-based estimation method.…”

Section: Data Mining In Effort Estimationmentioning

confidence: 99%

Application of data mining methods for effort estimation of software projects

2018

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Summary Information technology companies currently use data mining techniques in different areas with the goal of increasing the quality of decision‐making and to improve their business performance. The study described in this paper uses a data mining approach to produce an effort estimation of a software development process. It is based on data collected in a Croatian information technology company. The study examined 27 software projects with a total effort exceeding 42 000 work hours. The presented model employs a modified Cross‐Industry Standard Process for Data Mining, where prior to model creation, additional clustering of projects is performed. The results generated by the proposed approach generally had a smaller effort estimation error than the results of human experts. The applied approach has proved that sound results can be gained through the use of data mining within the studied area. As a result, it would be wise to use such estimates as additional input in the decision‐making process.

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“…Our fifth paper, "Negative Results for Software Effort Estimation", by Menzies et al (2016), seeks to assess whether new software effort estimation methods are actually better than older COCOMO-based methods initially proposed many years ago. Menzies et al show that Boehm's 2000 COCOMO II model (Boehm et al 2000) works as well (or better) than all approaches proposed since COCOMO II, for projects with enough data available to enable parametric estimation based on the 23 COCOMO attributes characterizing a software project.…”

Section: Summary Of Papersmentioning

confidence: 99%

Foreword to the special section on negative results in software engineering

2017

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Welcome to this special issue on Negative Results in Software Engineering. First, what do we mean by negative results? Negative or null results-that is, results which fail to show an effect-are all too uncommon in the published literature for many reasons, including publication bias and self-selection effects. Such results are nevertheless important in showing the research directions that did not pay off. In particular, "replication cannot be meaningful without the potential acknowledgment of failed replications" (Ferguson and Heene 2012). Negative Results in Software EngineeringWe believe negative results are especially important in software engineering, in order to firmly embrace the nature of experimentation in software research, just like most of us believe industry should do. This means scientific inquiry that is conducted along Lean Startup (Ries 2014) principles: start small, use validated learning and be prepared to 'pivot', or change course, if the learning outcome was negative. In this context, negative results are, given their methodology, failed approaches that are just as useful as successful approaches: they point out what hasn't worked, in order to redirect our collective scientific efforts. As Walter Tichy writes in Tichy (2000), "Negative results, if trustworthy, are extremely important for narrowing down the search space.

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Negative results for software effort estimation

Cited by 67 publications

References 63 publications

The state‐of‐the‐art in software development effort estimation

The state‐of‐the‐art in software development effort estimation

Application of data mining methods for effort estimation of software projects

Foreword to the special section on negative results in software engineering

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