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Background: There are too many design options for software effort estimators. How can we best explore them all? Aim: We seek aspects on general principles of effort estimation that can guide the design of effort estimators. Method: We identified the essential assumption of analogy-based effort estimation, i.e., the immediate neighbors of a project offer(More)
Background: Despite decades of research, there is no consensus on which software effort estimation methods produce the most accurate models. Aim: Prior work has reported that, given M estimation methods, no single method consistently outperforms all others. Perhaps rather than recommending one estimation method as best, it is wiser to generate estimates(More)
Cloud computing allows dynamic resource scaling for enterprise online transaction systems, one of the key characteristics that differentiates the cloud from the traditional computing paradigm. However, initializing a new virtual instance in a cloud is not instantaneous; cloud hosting platforms introduce several minutes delay in the hardware resource(More)
Several researchers have criticized the standards of performing and reporting empirical studies in software engineering. In order to address this problem, Jedlitschka and Pfahl have produced reporting guidelines for controlled experiments in software engineering. They pointed out that their guidelines needed evaluation. We agree that guidelines need to be(More)
Background: Do we always need complex methods for software effort estimation (SEE)? Aim: To characterize the essential content of SEE data, i.e., the least number of features and instances required to capture the information within SEE data. If the essential content is very small, then 1) the contained information must be very brief and 2) the value added(More)
Data-intensive analogy has been proposed as a means of software cost estimation as an alternative to other data intensive methods such as linear regression. Unfortunately, there are drawbacks to the method. There is no mechanism to assess its appropriateness for a specific dataset. In addition, heuristic algorithms are necessary to select the best set of(More)
Cloud computing has attracted attention as an important platform for software deployment, with perceived benefits such as elasticity to fluctuating load, and reduced operational costs compared to running in enterprise data centers. While some software is written from scratch specially for the cloud, many organizations also wish to migrate existing(More)
Analogy based estimation (ABE) generates an effort estimate for a new software project through adaptation of similar past projects (a.k.a. analogies). Majority of ABE methods follow uniform weighting in adaptation procedure. In this research we investigated non-uniform weighting through kernel density estimation. After an extensive experimentation of 19(More)
Background: Conclusion Instability in software effort estimation (SEE) refers to the inconsistent results produced by a diversity of predictors using different datasets. This is largely due to the “ranking instability” problem, which is highly related to the evaluation criteria and the subset of the data being used. Aim: To determine stable rankings of(More)
There is an emergence of Cloud application platforms such as Microsoft’s Azure, Google’s App Engine and Amazon’s EC2/SimpleDB/S3. Startups and Enterprise alike, lured by the promise of ‘infinite scalability’, ‘ease of development’, ‘low infrastructure setup cost’ are increasingly using(More)