A heuristic-based approach to code-smell detection

Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

A Process Modelling Success Model: Insights from a Case Study

Contemporary concepts such as Business Pro cess Re-engineering and Process Innovation emphasize the importance of process-oriented management concepts as a businesses paradigm. Large scaled multimillion-dollar implementations of Enterpri se Systems explicitly and implicitly state the importance of process modeling and its contribution to the success of these project. While there has been much research and publications on alterna tive process modeling techniques and tools, little attention has focused on post-hoc evaluation of actual process modeling activities or on deriving comprehensive guidelines on ‘how-to’ conduct process modeling effectively. This study aims at addressing this gap. A comprehensive a priori pro cess modeling success model has been derived and this paper reports on the results obtained from a detailed case study at a leading Australian logistics service provider, which was conducted with the aim of testing and re-specifying the model

Limited proficiency English teachers’ language use in science classrooms

The English for Teaching Mathematics and Science (ETeMS) policy was reversed in 2012 citing the reason that about 40% of the teachers were still using Malay in the ETeMS classroom hence, affecting the successful implementation of ETeMS. The quality of English used by the 60% and the other 40% especially in the rural areas motivates this study. Data for this investigation was obtained from three English teachers who have limited proficiency. These limited English proficiency (LEP) teachers teach science through English in a rural primary school in Malaysia. Transcripts of nine lessons, classroom observations and teacher interviews were gathered. The findings reveal that the English language used by the LEP teachers was simple and frequently riddled with errors which resulted in distortion of content taught. Errors were linked to negative transfers from Bahasa Melayu, teachers’ interlanguage, unsuccessful guesswork and memorizing words without full understanding of meaning. The LEP teachers therefore, made poor models for their students. The researcher concludes that even if the LEP teachers had striven to teach completely in English, the policy may have been seen to be implemented, but the quality of classroom discourse and content taught would have been problematic

Towards the ontology-based approach for factual information matching

Factual information is information based on facts or relating to facts. The reliability of automatically extracted facts is the main problem of processing factual information. The fact retrieval system remains one of the most effective tools for identifying the information for decision-making. In this work, we explore how can natural language processing methods and problem domain ontology help to check contradictions and mismatches in facts automatically