Synthesizing Perceived Challenges in Continuous Delivery : A Systematic Literature Review

Continuous delivery is an approach to software development which incorporates the practices, technologies and processes in order to achieve frequent delivery of valuable software to customers. Even though the continuous delivery approach has not existed very long yet, there has been quite a lot of a buzz around it and terms related to it (continuous deployment, deployment pipeline, and DevOps). Practices and benefits of the approach are presented in the literature, and organizations have been adopting it to a varying extent. However, as easy as the advocates of continuous delivery make the adoption look like, there have been reported challenges along the way. In order to focus research on finding the causes and creating solutions to these challenges, we must first identify them. To address this, we conducted a systematic literature review in order to collect perceived challenges related to the adoption of continuous delivery practices in software development projects, and analyzed the findings in order to provide synthesized information about these challenges. From among 13 publications 59 different challenges were identified which we categorized either as a social (procedural or organizational) or as a technical type of a challenge based on the evaluation of the findings. Among these challenges we found 14 more frequently occurring ones which also spanned across multiple software domains. We described these as common challenges. We also analyzed the reasons behind these challenges and identified five different themes (main reasons) that were immaturity, unsuitability, complexity, dependency, and security. We also analyzed how the software domain affected these reasons. Based on the observed mitigation strategies and research proposals, and our analysis, we proposed suggestions for future research directions. This study can be used as a support for finding future research directions regarding the challenges in the area of adopting continuous delivery practices in software development projects

Multiobjective optimization identifies cancer-selective combination therapies

Author summary Cancer is diagnosed in nearly 40% of people in the U.S at some point during their lifetimes. Despite decades of research to lower cancer incidence and mortality, cancer remains a leading cause of deaths worldwide. Therefore, new targeted therapies are required to further reduce the death rates and toxic effects of treatments. Here we developed a mathematical optimization framework for finding cancer-selective treatments that optimally use drugs and their combinations. The method uses multiobjective optimization to identify drug combinations that simultaneously show maximal therapeutic potential and minimal non-selectivity, to avoid severe side effects. Our systematic search approach is applicable to various cancer types and it enables optimization of combinations involving both targeted therapies as well as standard chemotherapies. Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.Peer reviewe