How to avoid drastic software process change (using stochastic stability)

Menzies, Tim; Williams, Steve; Boehm, Barry; Hihn, Jairus

doi:10.1109/icse.2009.5070552

Cited by 25 publications

(26 citation statements)

References 14 publications

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“…year at ICSP'08 and elsewhere [6][7][8]) we showed that this methods can yield estimates close to those seem using traditional methods, without requiring a time consuming data collection exercise.…”

Section: Fourth Reusementioning

confidence: 55%

“…-Simplistic linear extrapolation may be inappropriate when optimizing for effort and time and defects, there may be contradictory effects. For example, we have results where reducing effort leads to a dramatic increase in defects [8]. Hence, optimizing our models is not a simple matter of moving fixed distances over some linear effect: there are also some trade-offs to be considered (e.g.…”

Section: Case Studiesmentioning

confidence: 99%

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On the Relative Merits of Software Reuse

Orrego

El-Rawas

2009

Trustworthy Software Development Processes

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Abstract. Using process simulation and AI search methods, we compare software reuse against other possible changes to a project. such as reducing functionality or improving the skills of the programmer population. In one case, two generations of reuse were as good or better than any other project change (but a third and fourth generation of reuse was not useful). In another case, applying reuse to a project was demonstrable worse than several other possible changes to a project. Our conclusion is that the general claims regarding the benefits of software reuse do not hold for specific projects. We argue that the merits of software reuse need to be evaluated in a project by project basis. AI search over process models is useful for such an assessment, particularly when there is not sufficient data for precisely tuning a simulation model.

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Section: Fourth Reusementioning

confidence: 55%

Section: Case Studiesmentioning

confidence: 99%

On the Relative Merits of Software Reuse

Orrego

El-Rawas

2009

Trustworthy Software Development Processes

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“…Subsequent work demonstrated that the recommendations found in this way did better than numerous standard process improvement methods [20]. Later implementations were based on a state-of-the-art theorem prover [10].…”

Section: Seesawmentioning

confidence: 99%

“…SEESAW was first introduced in [18] and has been applied to numerous domains [10,16,17,[20][21][22]. W was first introduced in [8] but that report includes no comparisons with other quality optimizers.…”

Section: Introductionmentioning

confidence: 99%

Case-based reasoning vs parametric models for software quality optimization

Brady

2010

Proceedings of the 6th International Conference on Predictive Models in Software Engineering

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Background: There are many data mining methods but few comparisons between them. For example, there are at least two ways to build quality optimizers, programs that find project options that change quality measures like defects, development effort (total staff hours), and time (elapsed calendar months). In the first way, we construct a parametric model to represent prior software projects. In the second way, we just apply case-based reasoning to reason directly from historical cases.Aim: To assess case-based reasoning vs parametric modeling for quality optimization.Method: We compared the W case-based reasoner against the SEEWAW parametric modeling tool.Results: W is easy to explain and fast to build. It makes no parametric assumptions and hence can be rapidly applied to project data in many formats. SEESAW is an elaborate tool that can only process project data expressed in a particular ontology (i.e. just the COCOMO attributes). It is also slower to execute than W . In 24 different tests comparing W and SEESAW, W always performs at least as well as SEESAW. In 6 of those tests W performed statistically better (all tests used Mann-Whitney, 95% confidence). Lastly, like any CBR method, it comes with a built-in maintenance strategy (just add more cases).Conclusion: The W case-based reasoning tool is recommended over the SEESAW parametric modeling tool for purposes of quality optimization (except in the case where there is no local data).

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“…For example, in two studies [8], [9] we found that automated defect removal tools (such as those discussed at the ASE conference) were often required to achieve minimum defects/ development effort/ development time 1 . Elsewhere [10], [11], we have used this tool to comparatively assess different proposed changes to a project.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Value of Software Engineering Technologies

Green

Williams

El-Rawas

2009

2009 IEEE/ACM International Conference on Automated Software Engineering

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Abstract-When AI search methods are applied to software process models, then appropriate technologies can be discovered for a software project. We show that those recommendations are greatly affected by the business context of its use. For example, the automatic defect reduction tools explored by the ASE community are only relevant to a subset of software projects, and only according to certain value criteria. Therefore, when arguing for the value of a particular technology, that argument should include a description of the value function of the target user community.

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How to avoid drastic software process change (using stochastic stability)

Cited by 25 publications

References 14 publications

On the Relative Merits of Software Reuse

On the Relative Merits of Software Reuse

Case-based reasoning vs parametric models for software quality optimization

Understanding the Value of Software Engineering Technologies

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