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The diffusion of domotics solutions and of smart appliances and meters enables the monitoring of energy consumption at a very fine level and the development of forecasting and diagnostic applications. Anomaly detection (AD) in energy consumption data streams helps identify data points or intervals in which the behavior of an appliance deviates from normality and may prevent energy losses and break downs. Many statistical and learning approaches have been applied to the task, but the need remains of comparing their performances with data sets of different characteristics. This paper focuses on anomaly detection on quasi-periodic energy consumption data series and contrasts 12 statistical and machine learning algorithms tested in 144 different configurations on 3 data sets containing the power consumption signals of fridges. The assessment also evaluates the impact of the length of the series used for training and of the size of the sliding window employed to detect the anomalies. The generalization ability of the top five methods is also evaluated by applying them to an appliance different from that used for training. The results show that classical machine learning methods (Isolation Forest, One-Class SVM and Local Outlier Factor) outperform the best neural methods (GRU/LSTM autoencoder and multistep methods) and generalize better when applied to detect the anomalies of an appliance different from the one used for training.

The concurrent development of applications requires reconciling conflicting code updates by different developers. Recent research on the nature of merge conflicts in open source projects shows that a significant fraction of merge conflicts have limited size (one or two lines of code) and are resolved with simple strategies that use code present in the merged versions. Thus the opportunity arises of supporting the resolution of merge conflicts automatically by learning the way in which developers fix them. In this paper we propose a framework for automating the resolution of merge conflicts which learns from the resolutions made by developers and encodes such knowledge into conflict resolution rules applicable to conflicts not seen before. The proposed approach is text-based, does not depend on the programming languages of the merged files and exploits a well-known and general language (search and replacement regular expressions) to encode the conflict resolution rules. Evaluation results on 14,872 conflicts from 25 projects show that the system can synthesize a resolution for ≈ 49% of the conflicts occurred during the merge process (≈ 89% if one considers conflicts that have at least one similar conflict in the data set) and can reproduce exactly the same solution that human developers have applied in ≈ 55% of the cases (≈ 62% for single line conflicts).

Outdoor mobile applications are becoming popular in many fields, such as gaming, tourism and environment monitoring. They rely on the input of multiple, possibly noisy sensors, such as the camera, Global Positioning System (GPS), compass, accelerometer and gyroscope. Testing such applications requires the reproduction of the real conditions in which the application works, which are hard to recreate without automated support. This paper presents a capture & replay framework that automates the testing of mobile outdoor applications; the framework records in real-time data streams from multiple sensors acquired in field conditions, stores them, and let developers replay recorded test sequences in lab conditions, also computing quality metrics that help tracing soft errors.

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