Precise prediction of the goods demand is an important element of the supply chain management because we can optimise the level of stock based on predicted demand. Demand of goods may vary influenced by numerous factors, including price elasticity and weather, which we focus in this paper. We analysed daily sales data of consumer goods collected from Point of Sale (POS) systems of Japanese retailers, mostly supermarkets, which consist of records of price and quantity sold for each item, spanning several years. Demand may change according to regional preferences, so we built prediction models for each region by estimating demand curve of each item by employing linear regression and neural networks. We show that there are regional differences of the demand itself and also regional differences of the effect of weather.

Pointcut fragility is a well-documented problem in Aspect-Oriented Programming, changes to the base-code can lead to join points incorrectly falling in or out of the scope of pointcuts. Deciding which pointcuts have broken due to base-code changes is a daunting venture, especially in large and complex systems. We present an automated approach that recommends pointcuts that are likely to require modification due to a particular base-code change, as well as ones that do not. Our hypothesis is that join points selected by a pointcut exhibit common structural characteristics. Patterns describing such commonality are used to recommend pointcuts that have potentially broken to the developer. The approach is implemented as an extension to the popular Mylyn Eclipse IDE plug-in, which maintains focused contexts of entities relevant to the task at hand using a Degree of Interest (DOI) model.