SCOUT: A Multi-objective Method to Select Components in Designing Unit Testing

Context The source code of a mobile application has several components (i.e., code units) and they have a cost (e.g., test creation and execution) and benefit (e.g., find bug) value for performing unit testing. Problem Choosing in which components unit tests should be done, in order to increase the benefit (effectiveness) and decrease the cost of this activity. Proposal This study presents an approach that supports the selection of components for unit test creation in Android mobile applications. The SCUTMA approach allows the selection of components with respect to the following criteria, which can be combined: Cost of future maintenance, propensity to defects, frequency of call, risk of failure, market vulnerability and business value. Results Two empirical studies were performed to evaluate SCUTMA. The first study aimed at analyzing the correlation between the metrics that represent the criteria described above, where the result showed the possibility of using the metrics Cost of Future Maintenance (CFM), Code Smell (CS), Frequency of Calls (FC), Fault Risk (FR), Market Vulnerability (MV) and Business Value (BV) combined in a solution for component selection. The second study aimed to evaluate the effectiveness of using the SCUTMA approach. The result indicates that SCUTMA selected more components with error (58.33%) than the selection performed by specialists (40%). Conclusions The SCUTMA approach is effective in selecting components for creating unit tests in Android mobile applications.

Software metrics are created and used by the distinctive programming associations intended for assessing, guaranteeing program excellence, activity, and software recovery. Software metrics have turned into a basic part of programming growth and are utilized in each period of the product development life cycle. Software metrics essentially measure programming items like plan source code and help us in taking technical and administrative choices. The desire of this examination is to play out the relative investigation of static and dynamic metrics. In any case, software quality characteristics, for example, performance, execution time and dependability rely upon the dynamic exercises of the product artifact. Due to every one of these variables, we favor dynamic metrics instead of customary static metrics. With the assistance of customary static metrics, we are not capable to analyze different actualities of programming. There are various types of this OO static and dynamic equipments. In this paper we have played out a similar investigation of different OO static and dynamic metrics tools and find out the hybrid too is counted as best one extraction of both, static and dynamic characteristics from mobile Android applications. The source code and a Docker compartment is utilized by open source tool in only three phases pre-static, static and dynamic examination.

Summary form only given. Software metrics have become an integral part of software development and are used during every phase of the software development life cycle. Research in the area of software metrics tends to focus predominantly on static metrics that are obtained by static analysis of the software artifact. But software quality attributes such as performance and reliability depend on the dynamic behavior of the software artifact. Estimating software quality attributes based on dynamic metrics for the software system are more accurate and realistic. The research presented in this paper attempts to narrow the gap between static metrics and dynamic metrics, and lay the foundation for a more systematic approach to estimate the dynamic behavior of a software system early in the software development cycle. Focusing on coupling metrics, we present an empirical study to analyze the relationship between static and dynamic coupling metrics and propose the concept of pseudo dynamic metrics to estimate the dynamic behavior early in the software development lifecycle.

Software coupling is considered one of the most crucial issues in the software industry. The measurement of coupling gives a quantitative indication of internal features that are correlated to external quality attributes such as reliability, maintainability, extendibility, functionality and usability of the software. Various static and dynamic metrics have been proposed by several academicians and industry experts to evaluate software coupling. Although the values of static metrics can be easily extracted from the source code without its execution, researchers have emphasized the significance of dynamic metrics over static metrics in terms of program comprehensibility, change proneness, maintainability, etc. This chapter presents some of the dynamic coupling measures and their comparative analysis: (a) parametric-based analysis of six parameters, i.e. quality focus, measured entity, theoretical validation, dynamic analysis approach, environment, and statistical analysis technique; (b) functional-based analysis. Only those metrics which possess the required amount of information have been considered in order to make a meaningful comparison possible. Based on the comparison, the observed results reveal that empirical validation conducted for the evaluation of dynamic coupling metrics is not adequate to make them applicable to industries.

BackgroundConducting root cause analysis (RCA) is complex and challenging. The aim of this study was to better understand the experiences of RCA team members and how they value their involvement in the RCA to inform future recruitment, conduct and implementation of RCA findings into clinical practice.MethodsThe study was set in a health network in Adelaide, South Australia. A qualitative exploratory descriptive approach was undertaken to provide an in-depth understanding of team member’s experience in participating in an RCA. Eight of 27 RCA team members who conducted RCAs in the preceding 3-year period were included in one of three semi-structured focus groups. Thematic analysis was used to synthesise the transcribed data into themes.ResultsWe derived four major themes: Experiences and perceptions of the RCA team, Limitations of RCA recommendations, Facilitators and barriers to conducting an RCA, and Supporting colleagues involved in the adverse event. Participants’ mixed experience of RCAs ranged from enjoyment and the perception of worth and value to concerns about workload and lack of impact. Legislative privilege protecting RCAs from disclosure was both a facilitator and a barrier. Concern and a desire to better support their colleagues was widely reported.ConclusionsClinicians perceived value in reviewing significant adverse events. Improvements can be made in sharing learnings to make effective improvements in health care. We have proposed a process to better support interviewees and strengthen post interview follow up.

Software metrics help us to make meaningful estimates for software products and guide us in taking managerial and technical decisions like budget planning, cost estimation, quality assurance testing, software debugging, software performance optimization, and optimal personnel task assignments. Many design metrics have proposed in literature to measure various constructs of Object Oriented (OO) paradigm such as class, coupling, cohesion, inheritance, information hiding and polymorphism and use them further in determining the various aspects of software quality. However, the use of conventional static metrics have found to be inadequate for modern OO software due to the presence of run time polymorphism, templates class, template methods, dynamic binding and some code left unexecuted due to specific input conditions. This gap gave a cue to focus on the use of dynamic metrics instead of traditional static metrics to capture the software characteristics and further deploy them for maintainability predictions. As the dynamic metrics are more precise in capturing the execution behavior of the software system, in the current empirical investigation with the use of open source code, we validate and verify the superiority of dynamic metrics over static metrics. Four machine learning models are used for making the prediction model while training is performed simultaneously using static as well as dynamic metric suite. The results are analyzed using prevalent prediction accuracy measures which indicate that predictive capability of dynamic metrics is more concise than static metrics irrespective of any machine learning prediction model. Results of this would be helpful to practitioners as they can use the dynamic metrics in maintainability prediction in order to achieve precise planning of resource allocation.

The competitive environment is in turbulence and businesses are in a rapid digital and sustainable transition. This means challenges for organizations and the leadership of teams. Leadership is under uncertainties and pressure coming from inside and outside the team. Also, project team members face many stressful situations that cause uncertainty. Uncertainty stemming from the pressure of project work can be managed through social support. Projects are characterized by being time-limited and goal-oriented. For this reason, project teams face tough time pressures, which increase the workload of their members.Additionally, project teams are put together for a specific task, so their members may not have worked with each other before. A project team and even individual team members may work independently through self-organized tasks while having the same team objective. The basic nature of project work means that project team members are in a position from the beginning of a project where they can face quite a lot of uncertainty and stressful situations. In this work, social support refers to the means of interaction that alleviate the feelings of uncertainty that arise in stressful situations (Mikkola 2020). A project team tends to pay more attention to task-oriented processes, leaving less attention to relationship-centered interaction processes (DePoel et al. 2014), which include social support. Social support is known to have several positive effects in working life (Reinardy 2009).The goal of this research is to describe the perceptions and experiences of team members about social support. The following aspects of social support were analyzed: How is social support and its relevance understood? What factors promote and hinder social support?In this article social support is described from the perspective of project team members in working life. Because social support is constructed and mediated through interaction between project team members, it was essential to look at it through its manifestation.The research setting in this research is qualitative because it describes the team members’ perceptions and experiences. The goal of this research was to collect genuine opinions on this topic, so thematic individual interviews were conducted. A semi-structured thematic interview was chosen to hear people’s perceptions, which enabled a more in-depth treatment of the phenomenon. Social support’s manifestation in the project team was analyzed according to the perceptions and experiences of interviewees.The results of this research help to understand the importance of social support for project teams. The results increase understanding of how social support manifests itself in team interaction and what factors promote and hinder its delivery and application. In addition, they deepen the understanding of what social support is needed for and what implications it is seen to have from the perspective of team members.Ref.De Poel, F., Stoker, J., Van der Zee, K. 2014. Leadership and organizational tenure asymmetry as determinants of project team effectiveness. Group & Organization Management, 39 (1), 532–560.Mikkola, L. 2020. Supportive communication in the workplace. Mikkola, L. & Valo, M. (toim.) Workplace communication. New York: Routledge.Reinardy, S. 2009. Beyond Satisfaction: Journalists doubt career intentions as organizational support diminishes and job satisfaction declines. Atlantic Journal of Communication, 17 (3), 126–139.

Several important aspects of software product quality can be evaluated using dynamic metrics that effectively capture and reflect the software's true runtime behavior. While the extent of research in this field is still relatively limited, particularly when compared to research on static metrics, the field is growing, given the inherent advantages of dynamic metrics. The aim of this work is to systematically investigate the body of research on dynamic software metrics to identify issues associated with their selection, design and implementation. Mapping studies are being increasingly used in software engineering to characterize an emerging body of research and to identify gaps in the field under investigation. In this study we identified and evaluated 60 works based on a set of defined selection criteria. These studies were further classified and analyzed to identify their relativity to future dynamic metrics research. The classification was based on three different facets: research focus, research type and contribution type. We found a strong body of research related to dynamic coupling and cohesion metrics, with most works also addressing the abstract notion of software complexity. Specific opportunities for future work relate to a much broader range of quality dimensions.

Code inspection and unit testing are two popular fault- detecting techniques at unit level. Organizations where inspections are done generally supplement it with unit testing, as both are complementary. A natural question is the order in which the two techniques should be exercised as this may impact the overall effectiveness and efficiency of the verification process. In this paper, we present a controlled experiment comparing the two execution-orders, namely, code inspection followed by unit testing (CI-UT) and unit testing followed by code inspection (UT-CI), performed by a group of fresh software engineers in a company. The subjects inspected program-units by traversing a set of usage scenarios and applied unit testing by writing JUnit tests for the same. Our results showed that unit testing can be more effective, as well as more efficient, if applied after code inspection whereas the later is unaffected of the execution- order. Overall results suggest that sequence CI-UT performs better than UT-CI in time-constrained situations.

Which Audit Input Matters? An Analysis of the Determinants of Audit Quality, Profitability, and Audit Fees Using PCAOB Data

This paper reports on the feasibility of using eye tracking as a tool for students’ performance assessment in a medical serious game. We are particularly interested in analyzing the relationship between learners’ visual behaviour and their performance while solving medical cases. The objective of this study is twofold. First, we analyze how the students visually explore the learning environment across different areas of interest. Second, we examine whether static and dynamic eye tracking metrics can have an impact on students’ reasoning performance. Results revealed statistically significant associations between eye movement metrics and students’ outcomes. Particularly dynamic metrics better reflected students’ analytical reasoning abilities. Our findings have implications for the educational technology community seeking to gain a deeper understanding of the students’ learning experience.

704 Exploring the impact of human factors and hierarchy in communication and followership during paediatric and neonatal emergency situations: a qualitative study

The bearings of double toothed roller crushers in open pit mines are prone to failure during prolonged operation, resulting in reduced productivity and shortened equipment life. Therefore, a bearing fault prediction model based on asymmetric loss function gradient boosting algorithm and digital twin has been proposed for effective fault prediction of double toothed roller crusher bearings. First, the fault signal is extracted by Wigner-Ville time-frequency distribution and empirical mode decomposition. Then, the asymmetric loss function gradient boosting algorithm is used to model and predict the signal characteristics. Finally, a digital twin model is constructed to visualize bearing fault information by combining sensor data and historical fault data. The experimental results showed that the fault prediction model achieved a precision rate of 98%, a recall rate of 99%, and an F1 value of 98.5% in predicting bearing rolling element failures. Among them, the running time of fault prediction was 1 minute and 49s. The experimental results demonstrate that the proposed fault prediction model can learn complex feature representations from large amounts of data, with good fault prediction performance and efficiency, and has certain practical application value. The research results contribute to improving the accuracy and intelligence level of fault prediction methods, thereby ensuring the stable operation of open-pit mine double toothed roller crushers.

Dynamic metrics capture the run time features of object-oriented languages, i.e., runtime polymorphism, dynamic binding, etc., that are not covered by static metrics. Therefore, in this paper, we derived a new approach to measuring the software reusability of a design pattern based on dynamic metrics. To achieve this, the authors proposed a model based on five parameters, i.e., polymorphism, inheritance, number of children, coupling, and complexity, to measure the reusability factor by using various soft computing techniques, i.e., Fuzzy, Neural Network, and Neuro-Fuzzy. Further, we also compared the proposed model with four existing machine learning algorithms. Lastly, we found that the proposed model using the neuro-fuzzy technique is trained well and predicts well with MAE (Mean absolute error) 0.003 and RMSE (Root mean square error) 0.009 based on dynamic metrics. Hence, it is concluded that dynamic metrics are a better predictor of the reusability factor than static metrics.

The measurement is an important field in the software engineering. This field studies the assignment of a value to a software attribute using metrics. In fact, the use of metrics, which are the defined measurement methods and the measurement scales, in software engineering allows among other goals to control the quality of the software products, the quality of the software processes or the evolution of the software projects. Usually, we distinct two kinds of metrics: the static and the dynamic ones. The static metrics can be obtained from the source code of the software. On the other side, the dynamic metrics requires the execution of the software. Consequently, the static metrics are the most applied because of their relative simplicity compared to the dynamic metrics. However, we think that the dynamic metrics are more appropriate in some software categories. Multi-Agent Systems (MAS) is one of these categories because of the flexible nature of the agents. Consequently, we propose in this paper the using of the Aspect-Oriented Programming (AOP) to measure the dynamic metrics of multi-agent systems. In fact, the proposed approach provides several advantages like the simplicity, the reusability and the extensibility. In order to validate the proposed approach, we developed a tool that allows measuring of some metrics of DIMA-based applications.