DRIVE: Dockerfile Rule Mining and Violation Detection

A Dockerfile defines a set of instructions to build Docker images, which can then be instantiated to support containerized applications. Recent studies have revealed a considerable amount of quality issues with Dockerfiles. In this paper, we propose a novel approach DRIVE (Dockerfiles Rule mIning and Violation dEtection) to mine implicit rules and detect potential violations of such rules in Dockerfiles. DRIVE firstly parses Dockerfiles and transforms them to an intermediate representation. It then leverages an efficient sequential pattern mining algorithm to extract potential patterns. With heuristic-based reduction and moderate human intervention, potential rules are identified, which can then be utilized to detect potential violations of Dockerfiles. DRIVE identifies 34 semantic rules and 19 syntactic rules including 9 new semantic rules which have not been reported elsewhere. Extensive experiments on real-world Dockerfiles demonstrate the efficacy of our approach

Automatic Steering of Behavioral Model Inference

Many testing and analysis techniques use finite state mod-els to validate and verify the quality of software systems. Since the specification of such models is complex and time-consuming, researchers defined several techniques to extract finite state models from code and traces. Automatically generating models requires much less effort than designing them, and thus eases the verification and validation of large software systems. However, when models are inferred au-tomatically, the precision of the mining process is critical. Behavioral models mined with imprecise processes can in-clude many spurious behaviors, and can thus compromise the results of testing and analysis techniques that use those models. In this paper, we increase the precision of automata in-ferred from execution traces, by leveraging two learning tech-niques. We first mine execution traces to infer statistically significant temporal properties that capture relations be-tween non consecutive and possibly distant events. We then incrementally refine a simple initial automaton by merg-ing likely equivalent states. We identify equivalent states by analyzing set of consecutive events, and we use the in-ferred temporal properties to evaluate whether two equiv-alent states can be merged or not. We merge equivalent states only if the merging does violate any temporal prop-erty, since a merging that violates temporal properties is likely to introduce an imprecise generalization. Our gener-alization process that preserves temporal properties while merging states avoids breaking non-local relations, and thus solves one of the major cause of overgeneralized models. Thus, mined properties steer the learning of behavioral mod-els. The technique is completely automated and generates an automaton that both accepts the input traces and satis-fies the mined temporal properties. ∗This work has been partially supported by the Europea

Automatic Recommendation of API Methods from Feature Requests

MERLIO

Mining iterative generators and representative rules for software specification discovery

10.1109/TKDE.2010.24IEEE Transactions on Knowledge and Data Engineering232282-296ITKE

Locative Interaction In Urban Space: Programmatic Flexibility

Human spatial experience has recently expanded due to the development of location-aware technology. Locative information has become more significant within urban space; as such, related discourses have attempted to focus on the issue as a way in which we acquire locative information when we experience space. Digital location-aware methods enable the demonstration of live densities of telecommunication through which one can infer temporal and spatial factors of live urban situations. When locative telecommunication data is mapped onto urban space, temporal-spatial demographic maps are obtained. Based on these maps, one can infer the correlation between spatial experience and architectural programmes via on site observation and by determining the multi-layered structure of spatial experience via designed data installation. These considerations aim to investigate locative interaction in urban space in order to expand spatial experience. This research begins with two linked theoretical notions: rhythm analysis and heterotopia—in other words, temporality as it relates to our everyday life and spatiality as it relates to our search for ideal space. In addition to these positions, the following discourses are specifically developed to investigate locative interaction in urban space. Firstly, the temporal and spatial patterns of urban activities are investigated in an attempt to grasp current urban interactions. The telecommunication data is then mapped geographically. Secondly, the gap between the endowed architectural programmes and the observed activities in urban space is explored in order to examine the multi-layered structure of urban interaction. Thirdly, the above discussions are synthesised using a design project that interprets epistemic aspects of this initiative. Lastly, urban rhythms and locative virtual layers are suggested as the concept for locative interaction in urban space where architectural programmes become more flexible, thus expanding spatial experience. Two projects demonstrate as applicable scenarios of locative interaction in urban space; they involve a heterotopia finder and a floating gallery over London. This research suggests a new viewpoint from which to consider our world and its digital presence by mapping a ‘live urban space’ using telecommunication data—an initiative that highlights the importance of people as a crucial aspect of our digital surroundings. This research ultimately contributes to expanding urban spatial experience and providing an informative and holistic mapping structure for architecture and urban design, interweaving it with the digital environment