Optimizing requirements decisions with keys

Abstract. Solutions to non-linear requirements engineering problems may be "brittle"; i.e. small changes may dramatically alter solution effectiveness. Hence, it is not enough to just generate solutions to requirements problems-we must also assess solution robustness. The KEYS2 algorithm can generate decision ordering diagrams. Once generated, these diagrams can assess solution robustness in linear time. In experiments with real-world requirements engineering models, we show that KEYS2 can generate decision ordering diagrams in O(N 2 ). When assessed in terms of terms of (a) reducing inference times, (b) increasing solution quality, and (c) decreasing the variance of the generated solution, KEYS2 out-performs other search algorithms (simulated annealing, ASTAR, MaxWalkSat).

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“…-Finally, we offer some notes on related work and conclusions. This paper extends a prior publication [39] in several ways:…”

Section: Introductionsupporting

confidence: 64%

Finding robust solutions in requirements models

et al. 2009

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“…Algorithm 1. Power is based on BORE [32]. First, we assume that the target class is divided into one class as first and the other classes as rest.…”

Section: Cliffmentioning

confidence: 99%

“…Jalali et al [32] found that (1) was a poor ranking heuristic for low frequency evidence. To alleviate this problem, the support measure was introduced.…”

Section: Cliffmentioning

confidence: 99%

Balancing Privacy and Utility in Cross-Company Defect Prediction

Peters

Gong

Zhang

2013

IIEEE Trans. Software Eng.

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126

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Abstract-Background: Cross-company defect prediction (CCDP) is a field of study where an organization lacking enough local data can use data from other organizations for building defect predictors. To support CCDP, data must be shared. Such shared data must be privatized, but that privatization could severely damage the utility of the data. Aim: To enable effective defect prediction from shared data while preserving privacy. Method: We explore privatization algorithms that maintain class boundaries in a dataset. CLIFF is an instance pruner that deletes irrelevant examples. MORPH is a data mutator that moves the data a random distance, taking care not to cross class boundaries. CLIFF+MORPH are tested in a CCDP study among 10 defect datasets from the PROMISE data repository. Results: We find: 1) The CLIFFed+MORPHed algorithms provide more privacy than the state-of-the-art privacy algorithms; 2) in terms of utility measured by defect prediction, we find that CLIFF+MORPH performs significantly better. Conclusions: For the OO defect data studied here, data can be privatized and shared without a significant degradation in utility. To the best of our knowledge, this is the first published result where privatization does not compromise defect prediction.

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“…The SBSE approach can and has been applied to many problems in software engineering (e.g. requirements engineering [12]) but most often in the field of software testing [1]. Harmon's writing inspired us try simulated annealing to search the what-ifs in untuned COCOMO models [16].…”

Section: Related Workmentioning

confidence: 99%

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

Menzies

Williams

Boehm

et al. 2009

2009 IEEE 31st International Conference on Software Engineering

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Before performing drastic changes to a project, it is worthwhile to thoroughly explore the available options within the current structure of a project. An alternative to drastic change are internal changes that adjust current options within a software project. In this paper, we show that the effects of numerous internal changes can out-weigh the effects of drastic changes. That is, the benefits of drastic change can often be achieved without disrupting a project.The key to our technique is SEESAW, a novel stochastic stability tool that (a) considers a very large set of minor changes using stochastic sampling; and (b) carefully selects the right combination of effective minor changes.Our results show, using SEESAW, project managers have more project improvement options than they currently realize. This result should be welcome news to managers struggling to maintain control and continuity over their project in the face of multiple demands for drastic change.

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Optimizing requirements decisions with keys

Cited by 30 publications

References 33 publications

Finding robust solutions in requirements models

Finding robust solutions in requirements models

Balancing Privacy and Utility in Cross-Company Defect Prediction

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

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