Software selection in large-scale software engineering: A model and criteria based on interactive rapid reviews

CONTEXT

Software selection in large-scale software development continues to be ad hoc and ill-structured. Previous proposals for software component selection tend to be technology-specific and/or do not consider business or ecosystem concerns.

OBJECTIVE

Our main aim is to develop an industrially relevant technology-agnostic method that can support practitioners in making informed decisions when selecting software components for use in tools or in products based on a holistic perspective of the overall environment.

METHOD

We used method engineering to iteratively develop a software selection method for Ericsson AB based on a combination of published research and practitioner insights. We used interactive rapid reviews to systematically identify and analyse scientific literature and to support close cooperation and co-design with practitioners from Ericsson. The model has been validated through a focus group and by practical use at the case company.

RESULTS

The model consists of a high-level selection process and a wide range of criteria for assessing and for evaluating software to include in business products and tools.

CONCLUSIONS

We have developed an industrially relevant model for component selection through active engagement from a company. Co-designing the model based on previous knowledge demonstrates a viable approach to industry-academia collaboration and provides a practical solution that can support practitioners in making informed decisions based on a holistic analysis of business, organisation and technical factors.

Is There an Association Between Comprehensive Basic Science Examination Score and Acceptance into an Oral and Maxillofacial Surgery Residency Program?

Sparse data exist regarding the association between applicants' Comprehensive Basic Science Examination (CBSE) scores and acceptance into an oral and maxillofacial surgery (OMFS) residency program. The aims of this study were to assess the association between applicants' CBSE scores and acceptance into one OMFS residency program; to determine the association between other applicant-related variables and acceptance into the program; and to identify the mean CBSE score of accepted applicants by their dental schools' American Association of Oral and Maxillofacial Surgeons (AAOMS) district. For this retrospective data analysis conducted in 2017, the study sample consisted of applicants to the University of Illinois at Chicago OMFS residency program for the application cycles of 2013-14, 2014-15, and 2015-16. The primary predictor variable was CBSE score. The primary outcome variable was acceptance to the program. A total of 477 applicants were in the sample. In the multivariate analysis, CBSE score was found to be significantly associated with an increased chance of acceptance into the program (OR=1.11, 95% CI: 1.08, 1.15; p<0.001). Other variables associated with acceptance, though to a non-significant degree, were age (p=0.01), dental school grade point average (GPA) (p=0.01), and number of externships completed prior to application (p=0.02). These findings may be useful for both applicants and program directors.

Prediction of Software Reliability using Bio Inspired Soft Computing Techniques

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing techniques and assessment of soft computing techniques to predict reliability. The parameter considered while estimating and prediction of reliability are also discussed. This study can be used in estimation and prediction of the reliability of various instruments used in the medical system, software engineering, computer engineering and mechanical engineering also. These concepts can be applied to both software and hardware, to predict the reliability using CBSE.

Use of the National Board of Medical Examiners® Comprehensive Basic Science Exam: survey results of US medical schools

PURPOSE

The National Board of Medical Examiners^® (NBME) Comprehensive Basic Science Exam (CBSE) is a subject exam offered to US medical schools, where it has been used for external validation of student preparedness for the United States Medical Licensing Examination^® (USMLE) Step 1 in new schools and schools undergoing curricular reform. Information regarding the actual use of the NBME CBSE is limited. Therefore, the aim of the survey was to determine the scope and utilization of the NBME CBSE by US medical schools.

METHODS

A survey was sent in May 2016 to curriculum leadership of the 139 US medical schools listed on the Liaison Committee on Medical Education (LCME^®) website with provisional or full accreditation as of February 29, 2016. Responses were received from 53 schools (38% response rate). A series of different follow-up questions were asked if respondents stated "yes" or "no" to the initial question "Does your institution administer the NBME CBSE prior to the USMLE Step 1?".

RESULTS

A total of 37 schools (70%) administered the NBME CBSE. In all, 36 of the 37 schools responded to follow-up questions. Of 36 schools, 13 schools (36%) used the NBME CBSE for curriculum modification. Six schools (17%) used the NBME CBSE for formative assessment for a course, and five schools (14%) used the NBME CBSE for summative assessment for a course. A total of 28 schools (78%) used the NBME CBSE for identifying students performing below expectations and providing targeted intervention strategies. In all, 24 schools (67%) of the 36 responding schools administering the NBME CBSE administered the test once prior to the administration of the USMLE Step 1, whereas 10 (28%) schools administered the NBME CBSE two or more times prior to the administration of the USMLE Step 1.

CONCLUSION

Our data suggest that the NBME CBSE is administered by many US medical schools. However, the objective, timing, and number of exams administered vary greatly among schools.