3,525 research outputs found
Predicting and Evaluating Software Model Growth in the Automotive Industry
The size of a software artifact influences the software quality and impacts
the development process. In industry, when software size exceeds certain
thresholds, memory errors accumulate and development tools might not be able to
cope anymore, resulting in a lengthy program start up times, failing builds, or
memory problems at unpredictable times. Thus, foreseeing critical growth in
software modules meets a high demand in industrial practice. Predicting the
time when the size grows to the level where maintenance is needed prevents
unexpected efforts and helps to spot problematic artifacts before they become
critical.
Although the amount of prediction approaches in literature is vast, it is
unclear how well they fit with prerequisites and expectations from practice. In
this paper, we perform an industrial case study at an automotive manufacturer
to explore applicability and usability of prediction approaches in practice. In
a first step, we collect the most relevant prediction approaches from
literature, including both, approaches using statistics and machine learning.
Furthermore, we elicit expectations towards predictions from practitioners
using a survey and stakeholder workshops. At the same time, we measure software
size of 48 software artifacts by mining four years of revision history,
resulting in 4,547 data points. In the last step, we assess the applicability
of state-of-the-art prediction approaches using the collected data by
systematically analyzing how well they fulfill the practitioners' expectations.
Our main contribution is a comparison of commonly used prediction approaches
in a real world industrial setting while considering stakeholder expectations.
We show that the approaches provide significantly different results regarding
prediction accuracy and that the statistical approaches fit our data best
Maintainability and evolvability of control software in machine and plant manufacturing -- An industrial survey
Automated Production Systems (aPS) have lifetimes of up to 30-50 years,
throughout which the desired products change ever more frequently. This
requires flexible, reusable control software that can be easily maintained and
evolved. To evaluate selected criteria that are especially relevant for
maturity in software maintainability and evolvability of aPS, the approach
SWMAT4aPS+ builds on a questionnaire with 52 questions. The three main research
questions cover updates of software modules and success factors for both
cross-disciplinary development as well as reusable models. This paper presents
the evaluation results of 68 companies from machine and plant manufacturing
(MPM). Companies providing automation devices and/or engineering tools will be
able to identify challenges their customers in MPM face. Validity is ensured
through feedback of the participating companies and an analysis of the
statistical unambiguousness of the results. From a software or systems
engineering point of view, almost all criteria are fulfilled below
expectations
Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices
Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth
Generation (5G) mobile networks. MEC facilitates distributed cloud computing
capabilities and information technology service environment for applications
and services at the edges of mobile networks. This architectural modification
serves to reduce congestion, latency, and improve the performance of such edge
colocated applications and devices. In this paper, we demonstrate how reactive
service migration can be orchestrated for low-power MEC-enabled Internet of
Things (IoT) devices. Here, we use open-source Kubernetes as container
orchestration system. Our demo is based on traditional client-server system
from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As
the use case scenario, we post-process live video received over web real-time
communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1
handovers, demonstrating MEC-based software defined network (SDN). Now, edge
applications may reactively follow the UE within the radio access network
(RAN), expediting low-latency. The collected data is used to analyze the
benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end
(E2E) latency and power requirements of the UE are improved. We further discuss
the challenges of implementing such schemes and future research directions
therein
Generalized disjunction decomposition for evolvable hardware
Evolvable hardware (EHW) refers to self-reconfiguration hardware design, where the configuration is under the control of an evolutionary algorithm (EA). One of the main difficulties in using EHW to solve real-world problems is scalability, which limits the size of the circuit that may be evolved. This paper outlines a new type of decomposition strategy for EHW, the “generalized disjunction decomposition” (GDD), which allows the evolution of large circuits. The proposed method has been extensively tested, not only with multipliers and parity bit problems traditionally used in the EHW community, but also with logic circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library and randomly generated circuits. In order to achieve statistically relevant results, each analyzed logic circuit has been evolved 100 times, and the average of these results is presented and compared with other EHW techniques. This approach is necessary because of the probabilistic nature of EA; the same logic circuit may not be solved in the same way if tested several times. The proposed method has been examined in an extrinsic EHW system using theevolution strategy. The results obtained demonstrate that GDD significantly improves the evolution of logic circuits in terms of the number of generations, reduces computational time as it is able to reduce the required time for a single iteration of the EA, and enables the evolution of larger circuits never before evolved. In addition to the proposed method, a short overview of EHW systems together with the most recent applications in electrical circuit design is provided
Modularity and Architecture of PLC-based Software for Automated Production Systems: An analysis in industrial companies
Adaptive and flexible production systems require modular and reusable
software especially considering their long term life cycle of up to 50 years.
SWMAT4aPS, an approach to measure Software Maturity for automated Production
Systems is introduced. The approach identifies weaknesses and strengths of
various companie's solutions for modularity of software in the design of
automated Production Systems (aPS). At first, a self assessed questionnaire is
used to evaluate a large number of companies concerning their software
maturity. Secondly, we analyze PLC code, architectural levels, workflows and
abilities to configure code automatically out of engineering information in
four selected companies. In this paper, the questionnaire results from 16
German world leading companies in machine and plant manufacturing and four case
studies validating the results from the detailed analyses are introduced to
prove the applicability of the approach and give a survey of the state of the
art in industry
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