Machine Learning for Software Engineering: Models, Methods, and Applications

Machine Learning (ML) is the discipline that studies methods for automatically inferring models from data. Machine learning has been successfully applied in many areas of software engineering ranging from behaviour extraction, to testing, to bug fixing. Many more applications are yet be defined. However, a better understanding of ML methods, their assumptions and guarantees would help software engineers adopt and identify the appropriate methods for their desired applications. We argue that this choice can be guided by the models one seeks to infer. In this technical briefing, we review and reflect on the applications of ML for software engineering organised according to the models they produce and the methods they use. We introduce the principles of ML, give an overview of some key methods, and present examples of areas of software engineering benefiting from ML. We also discuss the open challenges for reaching the full potential of ML for software engineering and how ML can benefit from software engineering methods

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept to other positive variables of interest beyond the time domain. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Niño/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictor

Adapting weed management in rice to changing climates

This paper provides some of the scientific background on how projected environmental conditions could affect weeds and weed management in rice in Africa. Elevated CO2 levels may have positive effects on rice competitiveness with C4 weeds, but these are generally outnumbered by C3 species in weed populations of rice in Africa. Moreover, higher temperatures and drought will favor C4 over C3 plants. Increased CO2 levels may also improve tolerance of rice against parasitic weeds, while invasiveness of such species may be stimulated by soil degradation and more frequent droughts or floods. Elevated CO2 may increase belowground relative to aboveground growth, in particular of perennial (C3) species, rendering mechanical control less effective or even counterproductive. Increased CO2 levels, rainfall and temperature may also reduce the effectiveness of chemical control. The implementation of climate change adaptation technologies, such as drought-tolerant germplasm and water-saving irrigation regimes, will also have consequences for rice–weed competition. Rainfed production systems are hypothesized to be most vulnerable to direct effects of climate change (e.g. changes in rainfall patterns) and are likely to face increased competition from C4 and parasitic weeds. Bioticstress- tolerant rice cultivars to be developed for these systems should encompass weed competitiveness and parasitic-weed resistance. In irrigated systems, indirect effects will be more important and weed management strategies should be diversified to lessen dependency on herbicides and mechanical control, and be targeted to perennial rhizotomous (C3) weeds. Water-saving production methods that replace the weed-suppressive flood water layer by intermittent or continuous periods of aerobic conditions, necessitate additional weed management strategies to address the inherent increases in weed competition

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