MOTIVATION: COVID-19 has several distinct clinical phases: a viral replication phase, an inflammatory phase and in some patients, a hyper-inflammatory phase. High mortality is associated with patients developing cytokine storm syndrome. Treatment of hyper-inflammation in these patients using existing approved therapies with proven safety profiles could address the immediate need to reduce mortality. RESULTS: We analyzed the changes in the gene expression, pathways and putative mechanisms induced by SARS-CoV2 in NHBE, and A549 cells, as well as COVID-19 lung versus their respective controls. We used these changes to identify FDA approved drugs that could be repurposed to help COVID-19 patients with severe symptoms related to hyper-inflammation. We identified methylprednisolone (MP) as a potential leading therapy. The results were then confirmed in five independent validation datasets including Vero E6 cells, lung and intestinal organoids, as well as additional patient lung sample versus their respective controls. Finally, the efficacy of MP was validated in an independent clinical study. Thirty-day all-cause mortality occurred at a significantly lower rate in the MP-treated group compared to control group (29.6% versus 16.6%, P = 0.027). Clinical results confirmed the in silico prediction that MP could improve outcomes in severe cases of COVID-19. A low number needed to treat (NNT = 5) suggests MP may be more efficacious than dexamethasone or hydrocortisone. AVAILABILITY AND IMPLEMENTATION: iPathwayGuide is available at https://advaitabio.com/ipathwayguide/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

To evolve object-oriented code, one must understand both the code structure in terms of classes, and the runtime structure in terms of abstractions of objects that are being created and relations between those objects. To help with this understanding, static program analysis can extract heap abstractions such as object graphs. But the extracted graphs can become too large if they do not sufficiently abstract objects, or too imprecise if they abstract objects excessively to the point of being similar to a class diagram, where one box for a class represents all the instances of that class. One previously proposed solution uses both annotations and abstract interpretation to extract a global, hierarchical, abstract object graph that conveys both abstraction and design intent, but can still be related to the code structure. In this paper, we define metrics that relate nodes and edges in the object graph to elements in the code structure, to measure how they differ, and if the differences are indicative of language or design features such as encapsulation, polymorphism and inheritance. We compute the metrics across eight systems totaling over 100 KLOC, and show a statistically significant difference between the code and the object graph. In several cases, the magnitude of this difference is large.

During Architectural Risk Analysis (ARA), security architects use a runtime architecture to look for security vulnerabilities that are architectural flaws rather than coding defects. The current ARA process, however, is mostly informal and manual. In this paper, we propose Scoria, a semi-automated approach for finding architectural flaws. Scoria uses a sound, hierarchical object graph with abstract objects and dataflow edges, where edges can refer to nodes in the graph. The architects can augment the object graph with security properties, which can express security information unavailable in code. Scoria allows architects to write queries on the graph in terms of the hierarchy, reachability, and provenance of a dataflow object. Based on the query results, the architects enhance their knowledge of the system security and write expressive constraints. The expressiveness is richer than previous approaches that check only for the presence or absence of communication or do not track a dataflow as an object. To evaluate Scoria, we apply these constraints to several extended examples adapted from the CERT standard for Java to confirm that Scoria can detect injected architectural flaws. Next, we write constraints to enforce an Android security policy and find one architectural flaw in one Android application.

In this paper, we study the hidden dependencies that are a special kind of data flows. They play an important role in software maintenance and evolution because they propagate changes among the classes and they are hard to detect. We propose a technique that finds hidden dependencies through the computation of an “execute completely after” relation that is filtered using dynamically generated preconditions and postconditions. We show that these hidden dependencies exist even in well-structured software, like the open source programs JUnit, Drawlets, and Apache FtpServer. We also discuss the precision of this technique.

Partial comprehension is a necessity in the evolution and maintenance of very large software systems. The programmers form not only partial comprehension of the code, but also partial comprehension of the application domain. To describe the comprehension process, we introduce ontology fragments and investigate how programmers form, use and extend them before and during concept location; concept location is a prerequisite of code changes. We conducted case studies of concept location in two large systems, Eclipse and Mozilla, that both have more than 50,000 methods. Using grep search and ontology fragments, the programmers were able to locate the concepts after inspecting on average less than 10 methods and operating with ontology fragments of around 14 concepts, a very small fraction of the total.