With the wide adoption and acceptance of open source version control and hosting systems, more and more companies, including Google, Microsoft, Apple and Facebook, are putting their projects on such platforms, e.g., GitHub. It is very important for open source projects to be impactful, i.e., to attract attentions from the open source development community, so as to gain support on development, testing as well as maintenance from the community. However, the question of what factors affect open source project impact, remains largely open. Given the numerous confounding factors and the complex correlations among the factors, it is a challenge to answer the question. In this study, we gather a large dataset from GitHub and provide empirical insights on this question base on a data-driven approach. We randomly collect 146,286 projects from GitHub and then adopt data analysis techniques to automatically analyze the correlations of different features with the software project impact. We also provide suggestions on how to potentially make open source projects impactful base on our analysis results.

Distributed systems like cloud-based services are ever more popular. Assessing the reliability of distributed systems is highly non-trivial. Particularly, the order of executions among distributed components adds a dimension of non-determinism, which invalidates existing reliability assessment methods based on Markov chains. Probabilistic model checking based on models like Markov decision processes is designed to deal with scenarios involving both probabilistic behavior (e.g., reliabilities of system components) and non-determinism. However, its application is currently limited by state space explosion, which makes reliability assessment of distributed system particularly difficult. In this work, we improve the probabilistic model checking through a method of abstraction and reduction, which controls the communications among system components and actively reduces the size of each component. We prove the soundness and completeness of the proposed approach. Through an implementation in a software toolkit and evaluations with several systems, we show that our approach often reduces the size of the state space by several orders of magnitude, while still producing sound and accurate assessment.

Non-determinism in concurrent or distributed software systems (i.e., various possible execution orders among different distributed components) presents new challenges to the existing reliability analysis methods based on Markov chains. In this work, we present a toolkit RaPiD for the reliability analysis of non-deterministic systems. Taking Markov decision process as reliability model, RaPiD can help in the analysis of three fundamental and rewarding aspects regarding software reliability. First, to have reliability assurance on a system, RaPiD can synthesize the overall system reliability given the reliability values of system components. Second, given a requirement on the overall system reliability, RaPiD can distribute the reliability requirement to each component. Lastly, RaPiD can identify the component that affects the system reliability most significantly. RaPiD has been applied to analyze several real-world systems including a financial stock trading system, a proton therapy control system and an ambient assisted living room system.

Testing provides a probabilistic assurance of system correctness. In general, testing relies on the assumptions that the system under test is deterministic so that test cases can be sampled. However, a challenge arises when a system under test behaves non-deterministiclly in a dynamic operating environment because it will be unknown how to sample test cases.