Comparing and experimenting machine learning techniques for code smell detection

Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.     

Two CEGAR-based approaches for the safety verification of PLC-controlled plants

In this paper we address the safety analysis of chemical plants controlled by programmable logic controllers (PLCs). We consider a specification of the control program of the PLCs, extended with the specification of the dynamic plant behavior. The resulting hybrid models can be transformed to hybrid automata, for which advanced techniques for reachability analysis exist. However, the hybrid automata models are often too large to be analyzed. We propose two counterexample-guided abstraction refinement (CEGAR) approaches to keep the size of the hybrid models moderate.     

Socially shared regulation of learning in CSCL: understanding and prompting individual- and group-level shared regulatory activities

The field of computer supported collaborative learning (CSCL) is progressing instrumentally and theoretically. Nevertheless, few studies examine the effectiveness and efficiency of CSCL with respect to cognitive, motivational, emotional, and social issues, despite the fact that the role of regulatory processes is critical for the quality of students’ engagement in collaborative learning settings. We review the four earlier lines in developing support in CSCL and show how there has been a lack of work to support individuals in groups to engage in, sustain, and productively regulate their own and the group’s collaborative processes. Our aim is to discuss how our conceptual work in socially shared regulation of learning (SSRL) contributes to effective and efficient CSCL, what tools are presently available, and what the implications of research on these tools are for future tool development.     

ExoPranayama: a biofeedback-driven actuated environment for supporting yoga breathing practices

Both breathing and internal self-awareness are an integral part of any yoga practice. We describe and discuss the development of ExoPranayama, an actuated environment that physically manifests users’ breathing in yoga. Through a series of trials with yoga practitioners and expert teachers, we explore its role in the practice of yoga. Our interview results reveal that biofeedback through the environment supported teaching and improved self-awareness, but it impacted group cohesion. Two practical uses of the technology emerged for supporting breath control in yoga: (1) biofeedback can provide new information about users’ current internal states; (2) machine-driven feedback provides users with a future state or goal and leads to improved cohesiveness.     

Creativity Bento Box: A Physical Resource Pack to Support Interaction in Virtual Space

The Creativity Bento Box is a physical resource pack, designed to support casual social interaction and break taking in an intensive, computer-mediated social activity. It was developed within the Creativity Greenhouse project, which piloted a mechanism to create research proposals and distribute funding at a distance. This involved facilitated phases of collaboration and competition over multiple days of computer-mediated work, where participants communicate and interact through a virtual world. During the iterative development process, the lack of time for socializing, the intense focus on virtual resources, and a lack of time spent away from the screen were reported as negative issues in feedback from participants. This article reports on the development of the Creativity Bento Box and how it helped to address these issues. By providing physical resources that contrasted with the properties of the virtual world, it supported people to socialize and take breaks from their primary activity, allowed them to include physical space and artifacts in their interactions, and provoked moves away from the otherwise intense focus on the computer. The article reflects on the roles of the Bento Box as a gift, in bridging between physical and virtual contexts, its higher suitability during the earlier phases of ideation and group development, and its perception by participants as something “framed.” Through this, we highlight the underexplored potential of using physical, offline resources as a means to solve difficulties in distanced social interactions.     

Design and analysis of migration in parallel evolutionary algorithms

Parallelization is becoming a more important issue for solving difficult optimization problems. Island models combine phases of independent evolution with migration where genetic information is spread out to neighbored islands. This can lead to an increased diversity within the population. Despite many successful applications, the reasons behind the success of island models are not well understood. We perform a first rigorous runtime analysis for island models and construct a function where phases of independent evolution as well as communication among the islands are essential. A simple island model with migration finds a global optimum in polynomial time, while panmictic populations as well as island models without migration need exponential time, with very high probability. Our results lead to new insights into the usefulness of migration, how information is propagated in island models, and how to set parameters such as the migration interval. This is a novel contribution to the theoretical foundation of parallel EAs. Further, we provide empirical results that complement the theoretical results, investigate the robustness with respect to the choice of the migration interval and compare various migration topologies using statistical tests.     

A case study of factor influencing role improvisation in crisis response teams

Common characteristics of crisis situations are ambiguous and unplanned for events. The need for improvised roles can therefore be an imperative factor for the success of an operation. The aim of this study is to deepen the understanding of the processes taking place during improvised work “as it happens”. A case study of a crisis management team at work is presented and provides an in-depth analysis of the information and communication flow of persons acting in improvised roles, including contextual factors influencing the task at hand. The analysis suggests that three main factors lay behind decreased performance by the team when some of its members were forced to take on roles for which they lacked professional training; lack of language skills, lack of domain knowledge and insufficient organizational structure of the tasks. Based on the observations from this case study, we suggest three ways of improving a team’s performance and hence resilience when forced to improvise due to lack of personnel in one or more required competence areas. These are training to take on the responsibility for tasks or roles outside ones professional area of specialization, developing formal routines for changes in roles and tasks and developing and using tools and routines for information sharing.     

Non-encapsulated thermo-liquid crystals for digital particle tracking thermography/velocimetry in microfluidics

The ever accelerating state of technology has powered an increasing interest in heat transfer solutions and process engineering innovations in the microfluidics domain. In order to carry out such developments, reliable heat transfer diagnostic techniques are necessary. Thermo-liquid crystal (TLC) thermography, in combination with particle image velocimetry, has been a widely accepted and commonly used technique for the simultaneous measurement and characterization of temperature and velocity fields in macroscopic fluid flows for several decades. However, low seeding density, volume illumination, and low TLC particle image quality at high magnifications present unsurpassed challenges to its application to three-dimensional flows with microscopic dimensions. In this work, a measurement technique to evaluate the color response of individual non-encapsulated TLC particles is presented. A Shirasu porous glass membrane emulsification approach was used to produce the non-encapsulated TLC particles with a narrow size distribution and a multi-variable calibration procedure, making use of all three RGB and HSI color components, as well as the proper orthogonally decomposed RGB components, was used to achieve unprecedented low uncertainty levels in the temperature estimation of individual particles, opening the door to simultaneous temperature and velocity tracking using 3D velocimetry techniques.     

Usability study of ME2.0

Mobile context-aware applications execute in the background of hosts mobile devices. The applications source process and aggregate hosts’ contextual and personal information. This information is disclosed to ubiquitously pervasive services that adapt their offerings to individual preferences. Unfortunately, many developers continue to ignore the user perspective in context-aware application designs as they complicate their overall task and generate exponential requirements. The additional incorporation of privacy mechanisms in context-aware applications to safeguard context and personal information disclosures also complicates users’ tasks resulting to misconfigured or completely abandoned applications. Misconfigured applications give end-users a false assurance of privacy exposing them to comprising services. We present a usability study on Mobile Electronic Personality Version 2 a privacy enhanced context-aware mobile application for personalising ubiquitous services and adapting pervasive smart-spaces. We draw conclusions on key issues related to user needs, based on user interviews, surveys, prototypes and field evaluations. Users’ needs are evaluated against five themes, learn-ability, efficiency, memorability, errors, satisfaction and privacy contention. In addition, design layout preferences, privacy manageability and consensus design comprehension are also evaluated. Clarity of priorities in context-aware mobile applications shaped by usability studies effectively increases the acceptance of levels of potential users.     

A visual language for modeling and executing traceability queries

Current software and systems engineering tools provide only basic trace features, and as a result users are often compelled to construct non-trivial traceability queries using generic query languages such as SQL. In this paper, we present an alternative approach which defines traceability strategies for a project using UML class diagrams and then constructs trace queries as constraints upon subsets of the model. The visual trace modeling language (VTML) allows users to model a broad range of trace queries while hiding underlying technical details and data structures. The viability and expressiveness of VTML for use in a real project are demonstrated through modeling a broadly representative set of queries for a web-based health-care system. It is then evaluated through an experiment with human users to assess the readability and writability of VTML queries in comparison to generic SQL queries. We found that users read and constructed traceability queries considerably faster using VTML than using SQL. Furthermore, visually constructed traceability queries were substantially more correct compared to the same queries constructed with SQL.