Dany Moshkovich

Location and Context-Based Microservices for Mobile and Internet of Things Workloads

Research institutes such as Gartner and Forrester claim that the future of mobile will focus around the users context. Most of the future mobile applications will leverage user context to provide a richer user experience and deeper engagement, and consequently higher customer value. We present three cloud micro services that can substantially accelerate the development and evolvement of location and context-based applications. These include a contextual triggering micro service used to derive the users context in the moment of interaction, and visualization and analytics micro services to distill business and operational insights from application data. These micro services are described alongside mobile and Internet of Things usage examples.

Modernizing legacy software using a System Grokking technology

Reverse engineering is an essential part of the modernization process that enables the evolution of existing software assets. The extraction of state machines out of existing code is an important aspect of the reverse engineering process. However, none of the reverse engineering tools fully support an automatic extraction of state machines.

An empirical investigation of changes in some software properties over time

Software metrics are easy to define, but not so easy to justify. It is hard to prove that a metric is valid, i.e., that measured numerical values imply anything on the vaguely defined, yet crucial software properties such as complexity and maintainability. This paper employs statistical analysis and tests to check some plausible assumptions on the behavior of software and metrics measured for this software in retrospective on its versions evolution history. Among those are the reliability assumption implicit in the application of any code metric, and the assumption that the magnitude of change, i.e., increase or decrease of its size, in a software artifact is correlated with changes to its version number. Putting a suite of 36 metrics to the trial, we confirm most of the assumptions on a large repository of software artifacts. Surprisingly, we show that a substantial portion of the reliability of some metrics can be observed even in random changes to architecture. Another surprising result is that Boolean-valued metrics tend to flip their values more often in minor software version increments than in major increments.

Improving software through automatic untangling of cyclic dependencies

Cyclic dependencies among software components are considered an architectural problem that increases the development time and prevents proper reuse. One cause for the existence of such dependencies is the improper organization of elements into components. Optimal reorganization of the components that resolves the cyclic dependencies in large and complex software systems is extremely difficult to perform manually and is not computationally feasible to perform automatically. We present an approach for automatic untangling of cyclic dependencies among components for cycles of any size, having direct or transitive dependencies on one another. Our approach aims at minimizing the modifications to the original structure of the system, while taking into account various architectural properties. We evaluate our solution on twelve open source and three industrial applications. We demonstrate its applicability and value through architectural metrics and feedback from system architects.

How much information do software metrics contain

Software metrics computation and presentation are considered an important feature of many software design and development tools. The System Grokking Technology developed by IBM research enables investigation, validation and evolution of complex software systems at the level of abstraction suitable for human comprehension. As part of our ongoing effort to improve the tool and offer more useful abstractions we considered adorning the presented information with software metrics. The difficulty in doing that is in selecting among the legions of metrics competing for both scarce screen space and for the architects attention. In this paper, we describe a new criterion for evaluating the competing metrics based on a normalized version of Shannons information theoretical content. We also give values of these in a large software corpus and for a large set of metrics. Based on our measurements and this criterion, we can recommend the presentation of two metrics: module centrality, as measured by a variant of Googles classical page ranking algorithm, and module size, as measured by Chidamber and Kemerers WMC metric.