Search

Recently, many software organizations have been adopting Continuous Delivery and Continuous Deployment (CD) practices to develop and deliver quality software more frequently and reliably. Whilst an increasing amount of the literature covers different aspects of CD, little is known about the role of software architecture in CD and how an application should be (re-) architected to enable and support CD. We have conducted a mixed-methods empirical study that collected data through in-depth, semi-structured interviews with 21 industrial practitioners from 19 organizations, and a survey of 91 professional software practitioners. Based on a systematic and rigorous analysis of the gathered qualitative and quantitative data, we present a conceptual framework to support the process of (re-) architecting for CD. We provide evidence-based insights about practicing CD within monolithic systems and characterize the principle of "small and independent deployment units" as an alternative to the monoliths. Our framework supplements the architecting process in a CD context through introducing the quality attributes (e.g., resilience) that require more attention and demonstrating the strategies (e.g., prioritizing operations concerns) to design operations-friendly architectures. We discuss the key insights (e.g., monoliths and CD are not intrinsically oxymoronic) gained from our study and draw implications for research and practice., Comment: To appear in Empirical Software Engineering

In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations. Nevertheless, the dot product adopted in matrix factorization based recommender models does not satisfy the inequality property, which may limit their expressiveness and lead to sub-optimal solutions. To overcome this problem, we propose a novel recommender technique dubbed as {\em Metric Factorization}. We assume that users and items can be placed in a low dimensional space and their explicit closeness can be measured using Euclidean distance which satisfies the inequality property. To demonstrate its effectiveness, we further designed two variants of metric factorization with one for rating estimation and the other for personalized item ranking. Extensive experiments on a number of real-world datasets show that our approach outperforms existing state-of-the-art by a large margin on both rating prediction and item ranking tasks., Comment: 12 pages

Despite outperforming the human in many tasks, deep neural network models are also criticized for the lack of transparency and interpretability in decision making. The opaqueness results in uncertainty and low confidence when deploying such a model in model sharing scenarios, when the model is developed by a third party. For a supervised machine learning model, sharing training process including training data provides an effective way to gain trust and to better understand model predictions. However, it is not always possible to share all training data due to privacy and policy constraints. In this paper, we propose a method to disclose a small set of training data that is just sufficient for users to get the insight of a complicated model. The method constructs a boundary tree using selected training data and the tree is able to approximate the complicated model with high fidelity. We show that traversing data points in the tree gives users significantly better understanding of the model and paves the way for trustworthy model sharing., Comment: 9 pages, 10 figures

As platform ecosystems are growing by platform users' data, the importance of data governance has been highlighted. In particular, how to share control and decision rights with platform users are regarded as significant design issues since the role of them is increasing. Platform context should be considered when designing data governance in platform ecosystems (i.e. centralized/decentralized governance). However, there is limited research on this issue. Existing models focus on characteristics for enterprises. This results in limited support for platform ecosystems where there are different types of business context such as open strategies or platform maturity. This paper develops a contingency model for platform ecosystems including distinctive contingency factors. The study then discusses which data governance factors should be carefully considered and strengthened for each contingency in order to succeed in governance and to win market. A case study is performed to validate our model and to show its practical implications.

In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Experimental results on two real-world datasets demonstrate its state-of-the-art performances., Comment: 9 pages, ICONIP 2017

Recently, platform ecosystem has received attention as a key business concept. Sustainable growth of platform ecosystems is enabled by platform users supplying and/or demanding content from each other: e.g. Facebook, YouTube or Twitter. The importance and value of user data in platform ecosystems is accentuated since platform owners use and sell the data for their business. Serious concern is increasing about data misuse or abuse, privacy issues and revenue sharing between the different stakeholders. Traditional data governance focuses on generic goals and a universal approach to manage the data of an enterprise. It entails limited support for the complicated situation and relationship of a platform ecosystem where multiple participating parties contribute, use data and share profits. This article identifies data governance factors for platform ecosystems through literature review. The study then surveys the data governance state of practice of four platform ecosystems: Facebook, YouTube, EBay and Uber. Finally, 19 governance models in industry and academia are compared against our identified data governance factors for platform ecosystems to reveal the gaps and limitations., Comment: 12 pages

Blockchain technology promises a sizable potential for executing inter-organizational business processes without requiring a central party serving as a single point of trust (and failure). This paper analyzes its impact on business process management (BPM). We structure the discussion using two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle. This paper provides research directions for investigating the application of blockchain technology to BPM., Comment: Preprint for ACM TMIS

Context: Continuous practices, i.e., continuous integration, delivery, and deployment, are the software development industry practices that enable organizations to frequently and reliably release new features and products. With the increasing interest in and literature on continuous practices, it is important to systematically review and synthesize the approaches, tools, challenges, and practices reported for adopting and implementing continuous practices. Objective: This research aimed at systematically reviewing the state of the art of continuous practices to classify approaches and tools, identify challenges and practices in this regard, and identify the gaps for future research. Method: We used systematic literature review (SLR) method for reviewing the peer-reviewed papers on continuous practices published between 2004 and 1st June 2016. We applied thematic analysis method for analysing the data extracted from reviewing 69 papers selected using predefined criteria. Results: We have identified thirty approaches and associated tools, which facilitate the implementation of continuous practices in the following ways: (1) "reducing build and test time in continuous integration (CI)"; (2) "increasing visibility and awareness on build and test results in CI"; (3) "supporting (semi-) automated continuous testing"; (4) "detecting violations, flaws and faults in CI"; (5) "addressing security and scalability issues in deployment pipeline", and (6) "improving dependability and reliability of deployment process". We have also determined a list of critical factors such as "testing (effort and time)", "team awareness and transparency", "good design principles", "customer", "highly skilled and motivated team", "application domain", and "appropriate infrastructure" that should be carefully considered when introducing continuous practices in a given organization.

Eliminating duplicate data in primary storage of clouds increases the cost-efficiency of cloud service providers as well as reduces the cost of users for using cloud services. Existing primary deduplication techniques either use inline caching to exploit locality in primary workloads or use post-processing deduplication running in system idle time to avoid the negative impact on I/O performance. However, neither of them works well in the cloud servers running multiple services or applications for the following two reasons: Firstly, the temporal locality of duplicate data writes may not exist in some primary storage workloads thus inline caching often fails to achieve good deduplication ratio. Secondly, the post-processing deduplication allows duplicate data to be written into disks, therefore does not provide the benefit of I/O deduplication and requires high peak storage capacity. This paper presents HPDedup, a Hybrid Prioritized data Deduplication mechanism to deal with the storage system shared by applications running in co-located virtual machines or containers by fusing an inline and a post-processing process for exact deduplication. In the inline deduplication phase, HPDedup gives a fingerprint caching mechanism that estimates the temporal locality of duplicates in data streams from different VMs or applications and prioritizes the cache allocation for these streams based on the estimation. HPDedup also allows different deduplication threshold for streams based on their spatial locality to reduce the disk fragmentation. The post-processing phase removes duplicates whose fingerprints are not able to be cached due to the weak temporal locality from disks. Our experimental results show that HPDedup clearly outperforms the state-of-the-art primary storage deduplication techniques in terms of inline cache efficiency and primary deduplication efficiency., Comment: 14 pages, 11 figures, submitted to MSST2017