DeepSoft: A vision for a deep model of software

Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports.Comment: FSE 201

Discovering Software Reliability Patterns Based On Multiple Software Projects

Discovering patterns that indicate software reliability provides valuable information to software project managers. Software Quality Classification (SQC) modeling is a methodology that can be used to discover reliability patterns of large software projects. However, the patterns found by SQC modeling may not be accurate and robust owing to insufficient information used in the training process. This study compares two genetic programming-based SQC models using different volumes of data. These data were extracted from seven different NASA software projects. The results demonstrate that combining data from different projects can produce more accurate and reliable patterns

Clafer: Lightweight Modeling of Structure, Behaviour, and Variability

Embedded software is growing fast in size and complexity, leading to intimate mixture of complex architectures and complex control. Consequently, software specification requires modeling both structures and behaviour of systems. Unfortunately, existing languages do not integrate these aspects well, usually prioritizing one of them. It is common to develop a separate language for each of these facets. In this paper, we contribute Clafer: a small language that attempts to tackle this challenge. It combines rich structural modeling with state of the art behavioural formalisms. We are not aware of any other modeling language that seamlessly combines these facets common to system and software modeling. We show how Clafer, in a single unified syntax and semantics, allows capturing feature models (variability), component models, discrete control models (automata) and variability encompassing all these aspects. The language is built on top of first order logic with quantifiers over basic entities (for modeling structures) combined with linear temporal logic (for modeling behaviour). On top of this semantic foundation we build a simple but expressive syntax, enriched with carefully selected syntactic expansions that cover hierarchical modeling, associations, automata, scenarios, and Dwyer's property patterns. We evaluate Clafer using a power window case study, and comparing it against other notations that substantially overlap with its scope (SysML, AADL, Temporal OCL and Live Sequence Charts), discussing benefits and perils of using a single notation for the purpose

Software dependability modeling using an industry-standard architecture description language

Performing dependability evaluation along with other analyses at architectural level allows both making architectural tradeoffs and predicting the effects of architectural decisions on the dependability of an application. This paper gives guidelines for building architectural dependability models for software systems using the AADL (Architecture Analysis and Design Language). It presents reusable modeling patterns for fault-tolerant applications and shows how the presented patterns can be used in the context of a subsystem of a real-life application

Airborne antenna pattern calculations

Progress on the development of modeling software, testing software against caclulated data from program VPAP and measured patterns, and calculating roll plane patterns for general aviation aircraft is reported. Major objectives are the continued development of computer software for aircraft modeling and use of this software and program OSUVOL to calculate principal plane and volumetric radiation patterns. The determination of proper placement of antennas on aircraft to meet the requirements of the Microwave Landing System is discussed. An overview of the performed work, and an example of a roll plane model for the Piper PA-31T Cheyenne aircraft and the resulting calculated roll plane radiation pattern are included

Self-adaptive Software Modeling Based on Contextual Requirements

The ability of self-adaptive software in responding to change is determined by contextual requirements, i.e. a requirement in capturing relevant context-atributes and modeling behavior for system adaptation. However, in most cases, modeling for self-adaptive software is does not take into consider the requirements evolution based on contextual requirements. This paper introduces an approach through requirements modeling languages directed to adaptation patterns to support requirements evolution. The model is prepared through contextual requirements approach that is integrated into MAPE-K (monitor, anayze, plan, execute - knowledge) patterns in goal-oriented requirements engineering. As an evaluation, the adaptation process is modeled for cleaner robot. The experimental results show that the requirements modeling process has been able to direct software into self-adaptive capability and meet the requirements evolution

Implementation and Evaluation of Novel Buildstyles in Fused Deposition Modeling (FDM)

Previous investigations have shown that the optimization of extrusion dynamics in .conjunction with the buildstyle pattern is of paramount importance to increase part quality in Fused Deposition Modeling (FDM). Recently domain decomposition and space filling curves have been introduced for slice generation in FDM [1]. The current work focuses on the implementations of fractal-like buildstyle .patterns using. Simulated Annealing [2, 3], Lin-Kernighan algorithms [4] and Construction Procedures based on Nearest Neighbor Heuristics [5]. These computational optimization procedures are able to generate filling patterns that allow the continuous deposition of a single road to fill arbitrary shaped domains. The necessary software modules to produce arbitrary threedimensional artifacts have been developed and are evaluated with respect to part quality and build time.Mechanical Engineerin

Pattern Reification as the Basis for Description-Driven Systems

One of the main factors driving object-oriented software development for information systems is the requirement for systems to be tolerant to change. To address this issue in designing systems, this paper proposes a pattern-based, object-oriented, description-driven system (DDS) architecture as an extension to the standard UML four-layer meta-model. A DDS architecture is proposed in which aspects of both static and dynamic systems behavior can be captured via descriptive models and meta-models. The proposed architecture embodies four main elements - firstly, the adoption of a multi-layered meta-modeling architecture and reflective meta-level architecture, secondly the identification of four data modeling relationships that can be made explicit such that they can be modified dynamically, thirdly the identification of five design patterns which have emerged from practice and have proved essential in providing reusable building blocks for data management, and fourthly the encoding of the structural properties of the five design patterns by means of one fundamental pattern, the Graph pattern. A practical example of this philosophy, the CRISTAL project, is used to demonstrate the use of description-driven data objects to handle system evolution.Comment: 20 pages, 10 figure