Eclipse framework provides two interfaces: stable interfaces (APIs) and unstable interfaces (non-APIs). Despite the non-APIs being discouraged and unsupported, their usage is not uncommon. Previous studies showed that applications using relatively old non-APIs are more likely to be compatible with new releases compared to the ones that used newly introduced non-APIs; that the growth rate of non-APIs is nearly twice as much as that of APIs; and that the promotion of non-API to APIs happens at a slow pace since API providers have no assistance to identify public interface candidates. Motivated by these findings, our main aim was to empirically investigate the entire population (2,380K) of non-APIs to find the non-APIs that remain stable for a long period of time. We employ cross-project clone detection to identify whether non-APIs introduced in a given Eclipse release remain stable over successive releases. We provide a dataset of 327K stable non-API methods that can be used by both Eclipse interface providers as possible candidates of promotion. Instead of promoting non-APIs which are too fine-grained, we summarized the non-API methods groups in given classes that are stable together and present class-level non-APIs that possible candidates promotion. We have shown that it is possible to predict the stability of a non-API in subsequent Eclipse releases with a precision of ≥ 56%, a recall of ≥ 96% and an AUC of ≥ 92% and an Fmeasure of ≥ 81%. We have also shown that the metrics of length of a method and number of method parameters in a non-API method are very good predictors for the stability of the non-API in successive Eclipse releases. The results provided can help the API providers to estimate a priori how much work could be involved in performing the promotion.
In Africa, Uganda is among the countries with a high number of babies (20,000 babies) born with sickle cell, contributing between 6.8% of the children born with sickle cell every year worldwide and approximately 4.5% of the children born with hemoglobinopathies worldwide. It is estimated that by 2050, sickle cell cases will increase by 30% if no intervention is put in place. To facilitate early detection of sickle cell anaemia, medical experts employ machine learning algorithms to detect sickle cell abnormality. Previous research revealed that algorithms for recognizing shape of a sickle cell from blood smear by fractional dimension, cannot detect sickle cells if applied on blood samples containing overlapping red blood cells. In this research, the authors developed an algorithm to detect overlapping red blood cells for sickle cell disease diagnosis. The algorithm uses canny edge and double threshold machine learning techniques and takes overlapping red blood cells images as inputs to detect if these cells are sickle cell anaemic. These images have a scale magnification of (200×, 400×, 650×) pixel taken using a microscope. The algorithm was tested on a total of 1000 digital images and the overall accuracy, sensitivity and specificity were 98.18%, 98.29% and 97.98% respectively.
The Eclipse framework is a popular and widely used framework that has been evolving for over a decade. The framework provides both stable interfaces (APIs) and unstable interfaces (non-APIs). Despite being discouraged by Eclipse, client developers often use non-APIs which may cause their systems to fail when ported to new framework releases. To overcome this problem, Eclipse interface producers may promote unstable interfaces to APIs. However, client developers have no assistance to aid them to identify the promoted unstable interfaces in the Eclipse framework. We aim to help API users identify promoted unstable interfaces. We used the clone detection technique to identify promoted unstable interfaces as the framework evolves. Our empirical investigation on 16 Eclipse major releases presents the following observations. First, we have discovered that there exists over 60% non-API methods of the total interfaces in each of the analyzed 16 Eclipse releases. Second, we have discovered that the percentage of promoted non-APIs identified through clone detection ranges from 0.20% to 10.38%.
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