A Model-Based Approach to Managing Feature Binding Time in Software Product Line Engineering

Software Product Line Engineering (SPLE) is a software reuse paradigm for developing software products, from managed reusable assets, based on analysis of commonality and variability (C & V) of a product line. Many approaches of SPLE use a feature as a key abstraction to capture the C&V. Recently, there have been increasing demands for the provision of flexibility about not only the variability of features but also the variability of when features should be selected (i.e., variability on feature binding times). Current approaches to support variations of feature binding time mostly focused on ad hoc implementation mechanisms. In this paper, we first identify the challenges of feature binding time management and then propose an approach to analyze the variation of feature binding times and use the results to specify model-based architectural components for the product line. Based on the specification, components implementing variable features are parameterized with the binding times and the source codes for the components and the connection between them are generated

Adopting an Agile Approach for Reflective Learning and Teaching

Software engineering is concerned with how best to create software in ways that promote sustainable development and maximise quality. We have been largely successful at transferring software engineering knowledge into the industry, however, many challenges in software engineering training remain. A key amongst these is how best to teach practical engineering approaches along with the theoretical concepts behind them. This paper describes our experience of adopting an agile approach for reflective learning and teaching within the context of our Software Systems Engineering module, aimed at addressing challenges identified with previous efforts to promote reflective practice. Our study attempts to strengthen the use of reflective learning approaches for our current cohort, as well as introducing reflective teaching practices, whereby we examine our teaching approach in order to improve its efficiency and effectiveness. Our analysis of student response to the module shows that it was very well-received by the students, and we were able to collect ample evidence from feedback to support this. Most of our approaches resulted in positive feedback and contributed to improvements in teaching quality, however, we also identified some key aspects in our method that could still benefit from refinement, such as the need for explicit links between learning outcomes and workshop activities, and intuitive design of feedback questions, along with feedback collection frequency. We plan to incorporate these additional updates into the revision of the module for the next academic year, and to continue collecting and analysing feedback data for further enhancement

What Happens in Peer-Support, Stays in Peer-Support: Software Architecture for Peer-Sourcing in Mental Health

Digital health technology utilizing wearables, IoT and mobile devices has been successfully applied in the monitoring of numerous diseases and conditions. However, intervention, in response to monitored data, is yet to benefit from technological support and continues to follow a traditional point-of-care delivery model by providers and health professionals. Mental health is an example of a critical health area in dire need for technology solutions to enable timely, effective and scalable interventions. This is especially the case with an increasing prevalence of mental health conditions and a declining capacity of the healthcare professional workforce. Numerous studies reveal the potential for peer support groups as an effective, scalable, cost-effective, first-line of response in mental health interventions. Peer support helps participants, at low and moderate risk, better understand their diseases or conditions and empowers them to take control of their own health. Peer support interactions also seems to inform health professionals with insights and intricate knowledge, making it effectively a learning health system. This paper proposes a software architecture to better enable "peer-sourcing". We present related work and show how the proposed architecture might draw similarity to and differences from crowd-sourcing architectures. We also present a study in which we interacted with service users (mental health patients) and mental healthcare professionals to better understand and elicit the key requirements for the software architecture

Identifying Design Problems in the Source Code:A Grounded Theory

The prevalence of design problems may cause re-engineering or even discontinuation of the system. Due to missing, informal or outdated design documentation, developers often have to rely on the source code to identify design problems. Therefore, developers have to analyze different symptoms that manifest in several code elements, which may quickly turn into a complex task. Although researchers have been investigating techniques to help developers in identifying design problems, there is little knowledge on how developers actually proceed to identify design problems. In order to tackle this problem, we conducted a multi-trial industrial experiment with professionals from 5 software companies to build a grounded theory. The resulting theory offers explanations on how developers identify design problems in practice. For instance, it reveals the characteristics of symptoms that developers consider helpful. Moreover, developers often combine different types of symptoms to identify a single design problem. This knowledge serves as a basis to further understand the phenomena and advance towards more effective identification techniques

Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis

BackgroundMagnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI.MethodsThis study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation.ResultsA total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705–0.745), 0.936 (95% CI, 0.924–0.947), and 0.830 (95%CI, 0.792–0.868), respectively, at the image level and 0.947 (95% CI, 0.912–0.982), 0.691 (95% CI, 0.603–0.779), and 0.816 (95% CI, 0.776–0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493–0.780), 0.944 (95% CI, 0.933–0.955), and 0.731 (95% CI, 0.681–0.780), respectively, at the image level and 0.806 (95% CI, 0.729–0.883), 0.617 (95% CI, 0.523–0.711), and 0.711 (95% CI, 0.660–0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation.ConclusionAn AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting

Correlation of DEFA1 Gene Copy Number Variation with Intestinal Involvement in Behcet's Disease

Copy number variation has been associated with various autoimmune diseases. We investigated the copy number (CN) of the DEFA1 gene encoding α-defensin-1 in samples from Korean individuals with Behcet's disease (BD) compared to healthy controls (HC). We recruited 55 BD patients and 35 HC. A duplex Taqman® real-time PCR assay was used to assess CN. Most samples (31.1%) had a CN of 5 with a mean CN of 5.4 ± 0.2. There was no significant difference in the CN of the DEFA1 gene between BD patients and HC. A high DEFA1 gene CN was significantly associated with intestinal involvement in BD patients. Variable DEFA1 gene CNs were observed in both BD patients and HC and a high DEFA1 gene CN may be associated with susceptibility to intestinal involvement in BD

Extracellular High-Mobility Group Box 1 is Increased in Patients with Behçet's Disease with Intestinal Involvement

High-mobility group box 1 (HMGB1) protein has been demonstrated to play an important role in chronic inflammatory diseases including rheumatoid arthritis, and systemic lupus erythematosus. This study investigated the association between extracellular HMGB1 expression and disease activity, and clinical features of Behçet's disease (BD). Extracellular HMGB1 expression in the sera of 42 BD patients was measured and was compared to that of 22 age- and sex-matched healthy controls. HMGB1 expression was significantly increased in BD patients compared to healthy controls (78.70 ± 20.22 vs 10.79 ± 1.90 ng/mL, P = 0.002). In addition, HMGB1 expression was significantly elevated in BD patients with intestinal involvement compared to those without (179.61 ± 67.95 vs 61.89 ± 19.81 ng/mL, P = 0.04). No significant association was observed between HMGB1 concentration and other clinical manifestations, or disease activity. It is suggested that extracellular HMGB1 may play an important role in the pathogenesis of BD

Discovering context-specific relationships from biological literature by using multi-level context terms

<p>Abstract</p> <p>Background</p> <p>The Swanson's ABC model is powerful to infer hidden relationships buried in biological literature. However, the model is inadequate to infer relations with context information. In addition, the model generates a very large amount of candidates from biological text, and it is a semi-automatic, labor-intensive technique requiring human expert's manual input. To tackle these problems, we incorporate context terms to infer relations between AB interactions and BC interactions.</p> <p>Methods</p> <p>We propose 3 steps to discover meaningful hidden relationships between drugs and diseases: 1) multi-level (gene, drug, disease, symptom) entity recognition, 2) interaction extraction (drug-gene, gene-disease) from literature, 3) context vector based similarity score calculation. Subsequently, we evaluate our hypothesis with the datasets of the "Alzheimer's disease" related 77,711 PubMed abstracts. As golden standards, PharmGKB and CTD databases are used. Evaluation is conducted in 2 ways: first, comparing precision of the proposed method and the previous method and second, analysing top 10 ranked results to examine whether highly ranked interactions are truly meaningful or not.</p> <p>Results</p> <p>The results indicate that context-based relation inference achieved better precision than the previous ABC model approach. The literature analysis also shows that interactions inferred by the context-based approach are more meaningful than interactions by the previous ABC model.</p> <p>Conclusions</p> <p>We propose a novel interaction inference technique that incorporates context term vectors into the ABC model to discover meaningful hidden relationships. By utilizing multi-level context terms, our model shows better performance than the previous ABC model.</p