Jacky W. Keung

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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)
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)
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)
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)
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)
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)
Collecting the data required for quality prediction within a development team is time-consuming and expensive. An alternative to make predictions using data that crosses from other projects or even other companies. We show that with/without relevancy filtering, imported data performs the same/worse (respectively) than using local data. Therefore, we(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)
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)