Leandro L. Minku

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Dynamic Adaptive Automated Software Engineering (DAASE)

Principal investigator: Prof. Xin Yao.
Keywords: software project estimation, project scheduling problem, ensembles of learning machines, online learning, concept drift, evolutionary algorithms.

Funding: Engineering and Physical Sciences Research Council (EPSRC).

DAASE aims to create a new approach to software engineering which places computational search at the heart of the process and products it creates and embeds adaptivity into both. This new approach will produce software that is dynamically adaptive, being not only able to respond to and fix problems that arise before deployment and during operation, but also to continually optimise, re-configure and evolve to adapt to new operating conditions, platforms and environmental challenges. DAASE will create an array of new processes, methods, techniques and tools for this new kind of software engineering, radically transforming both theory and practice of software engineering. As part of it, DAASE will develop a hyper-heuristic approach to adaptive automation. A hyper-heuristic is a methodology for selecting or generating heuristics. Most heuristic methods in the literature operate on a search space of potential solutions to a particular problem. However, a hyper-heuristic operates on a search space of heuristics.

Currently, I am researching into adaptive software prediction. Software prediction tasks are of strategic importance for software developing companies. An example of such task is software effort estimation. Overestimations may result in a company loosing contracts or wasting resources, whereas underestimations may result in poor quality, delayed or unfinished software systems. Most software prediction research neglects the fact that software prediction tasks operate in online changing environments. Models are typically trained on a set of projects and evaluated on another set of projects, without considering whether the training projects were really available before the testing projects. Besides possibly leading to incorrect conclusions, this results in inflexible prediction approaches that become obsolete with time. I am currently investigating the type of changes suffered by software prediction tasks and proposing new approaches to quickly adapt to these changes.

I have published the following paper on this topic at PROMISE'2012:

Can Cross-company Data Improve Performance in Software Effort Estimation?
Authors: Leandro L. Minku and Xin Yao

Background: There has been a long debate in the software engineering literature concerning how useful cross-company (CC) data are for software effort estimation (SEE) in comparison to within-company (WC) data. Studies indicate that models trained on CC data obtain either similar or worse performance than models trained solely on WC data.

Aims: We aim at investigating if CC data could help to increase performance and under what conditions.

Method: The work concentrates on the fact that SEE is a class of online learning tasks which operate in changing environments, even though most work so far has neglected that. We conduct an analysis based on the performance of different approaches considering CC and WC data. These are: (1) an approach not designed for changing environments, (2) approaches designed for changing environments and (3) a new online learning approach able to identify when CC data are helpful or detrimental.

Results: Interesting features of data sets commonly used in the SEE literature are revealed, showing that different subsets of CC data can be beneficial or detrimental depending on the moment in time. The newly proposed approach is able to benefit from that, successfully using CC data to improve performance over WC models.

Conclusions: This work not only shows that CC data can help to increase performance for SEE tasks, but also demonstrates that the online nature of software prediction tasks should be exploited, being an important issue to be considered in the future.