A comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production lines

The present work is intended for residual oxide scale detection and classification through the application of image processing techniques. This is a defect that can remain in the surface of stainless steel coils after an incomplete pickling process in a production line. From a previous detailed study over reflectance of residual oxide defect, we present a comparative study of algorithms for image segmentation based on thresholding methods. In particular, two computational models based on multi-linear regression and neural networks will be proposed. A system based on conventional area camera with a special lighting was installed and fully integrated in an annealing and pickling line for model testing purposes. Finally, model approaches will be compared and evaluated their performance..Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A Critical Review of "Automatic Patch Generation Learned from Human-Written Patches": Essay on the Problem Statement and the Evaluation of Automatic Software Repair

At ICSE'2013, there was the first session ever dedicated to automatic program repair. In this session, Kim et al. presented PAR, a novel template-based approach for fixing Java bugs. We strongly disagree with key points of this paper. Our critical review has two goals. First, we aim at explaining why we disagree with Kim and colleagues and why the reasons behind this disagreement are important for research on automatic software repair in general. Second, we aim at contributing to the field with a clarification of the essential ideas behind automatic software repair. In particular we discuss the main evaluation criteria of automatic software repair: understandability, correctness and completeness. We show that depending on how one sets up the repair scenario, the evaluation goals may be contradictory. Eventually, we discuss the nature of fix acceptability and its relation to the notion of software correctness.Comment: ICSE 2014, India (2014

Software fault characteristics: A synthesis of the literature

Faults continue to be a significant problem in software. Understanding the nature of these faults is important for practitioners and researchers. There are many published fault characteristics schemes but no one scheme dominates. Consequently it is difficult for practitioners to effectively evaluate the nature of faults in their software systems, and it is difficult for researchers to compare the types of faults found by different fault detection techniques. In this paper we synthesise previous fault characteristics schemes into one comprehensive scheme. Our scheme provides a richer view of faults than the previous schemes published and presents a comprehensive, unified approach which accommodates the many previous schemes. A characteristics-based view of faults should be considered by future researchers in the analysis of software faults and in the design and evaluation of new fault detection tools. We recommend that our fault characteristics scheme be used as a benchmark scheme

A general software defect-proneness prediction framework

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.BACKGROUND - Predicting defect-prone software components is an economically important activity and so has received a good deal of attention. However, making sense of the many, and sometimes seemingly inconsistent, results is difficult. OBJECTIVE - We propose and evaluate a general framework for software defect prediction that supports 1) unbiased and 2) comprehensive comparison between competing prediction systems. METHOD - The framework is comprised of 1) scheme evaluation and 2) defect prediction components. The scheme evaluation analyzes the prediction performance of competing learning schemes for given historical data sets. The defect predictor builds models according to the evaluated learning scheme and predicts software defects with new data according to the constructed model. In order to demonstrate the performance of the proposed framework, we use both simulation and publicly available software defect data sets. RESULTS - The results show that we should choose different learning schemes for different data sets (i.e., no scheme dominates), that small details in conducting how evaluations are conducted can completely reverse findings, and last, that our proposed framework is more effective and less prone to bias than previous approaches. CONCLUSIONS - Failure to properly or fully evaluate a learning scheme can be misleading; however, these problems may be overcome by our proposed framework.National Natural Science Foundation of Chin