215,092 research outputs found
IN2GESOFT: Innovation and Integration of Methods for the Development and Quantitative Management of Software Projects TIN2004-06689-C03
This coordinated project intends to introduce new methods in software engineering
project management, integrating different quantitative and qualitative technologies in the
management processes. The underlying goal to all three subprojects participants is the
generation of information adapted for the efficient performance in the directing of the
project. The topics that are investigated are related to the capture of decisions in dynam ical environments and complex systems, software testing and the analysis of the manage ment strategies for the process assessment of the software in its different phases of the
production.
The project sets up a methodological, conceptual framework and supporting tools that
facilitate the decision making in the software project management. This allows us to eval uate the risk and uncertainty associated to different alternatives of management before
leading them to action. Thus, it is necessary to define a taxonomy of software models
so that they reflect the current reality of the projects. Since the software testing is one
of the most critical and costly processes directed to guarantee the quality and reliability
of the software, we undertake the research on the automation of the process of software
testing by means of the development of new technologies test case generation, mainly
based in metaheuristic and model checking techniques in the domains of database and
internet applications. The software system developed will allow the integration of these
technologies, and the management information needed, from the first phases of the cycle
of life in the construction of a software product up to the last ones such as regression tests
and maintenance.
The set of technologies that we investigate include the use of statistical analysis and of
experimental design for obtaining metrics in the phase of analysis, the application of the bayesian nets to the decision processes, the application of the standards of process eval uation and quality models, the utilization of metaheuristics algorithms and technologies
of prediction to optimize resources, the technologies of visualization to construct control
dashboards, hybrid models for the simulation of processes and others
A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency
In this paper, we address the problem of asset performance monitoring, with the intention
of both detecting any potential reliability problem and predicting any loss of energy consumption
e ciency. This is an important concern for many industries and utilities with very intensive
capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an
approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically
with Association Rule (AR) Mining. The combination of these two techniques can now be done
using software which can handle large volumes of data (big data), but the process still needs to
ensure that the required amount of data will be available during the assets’ life cycle and that its
quality is acceptable. The combination of these two techniques in the proposed sequence di ers
from previous works found in the literature, giving researchers new options to face the problem.
Practical implementation of the proposed approach may lead to novel predictive maintenance models
(emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of
performance and help manage assets’ O&M accordingly. The approach is illustrated using specific
examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-
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
The Co-Evolution of Test Maintenance and Code Maintenance through the lens of Fine-Grained Semantic Changes
Automatic testing is a widely adopted technique for improving software
quality. Software developers add, remove and update test methods and test
classes as part of the software development process as well as during the
evolution phase, following the initial release. In this work we conduct a large
scale study of 61 popular open source projects and report the relationships we
have established between test maintenance, production code maintenance, and
semantic changes (e.g, statement added, method removed, etc.). performed in
developers' commits.
We build predictive models, and show that the number of tests in a software
project can be well predicted by employing code maintenance profiles (i.e., how
many commits were performed in each of the maintenance activities: corrective,
perfective, adaptive). Our findings also reveal that more often than not,
developers perform code fixes without performing complementary test maintenance
in the same commit (e.g., update an existing test or add a new one). When
developers do perform test maintenance, it is likely to be affected by the
semantic changes they perform as part of their commit.
Our work is based on studying 61 popular open source projects, comprised of
over 240,000 commits consisting of over 16,000,000 semantic change type
instances, performed by over 4,000 software engineers.Comment: postprint, ICSME 201
Development of the Integrated Model of the Automotive Product Quality Assessment
Issues on building an integrated model of the automotive product quality assessment are studied herein basing on widely applicable methods and models of the quality assessment. A conceptual model of the automotive product quality system meeting customer requirements has been developed. Typical characteristics of modern industrial production are an increase in the production dynamism that determines the product properties; a continuous increase in the volume of information required for decision-making, an increased role of knowledge and high technologies implementing absolutely new scientific and technical ideas. To solve the problem of increasing the automotive product quality, a conceptual structural and hierarchical model is offered to ensure its quality as a closed system with feedback between the regulatory, manufacturing, and information modules, responsible for formation of the product quality at all stages of its life cycle. The three module model of the system of the industrial product quality assurance is considered to be universal and to give the opportunity to explore processes of any complexity while solving theoretical and practical problems of the quality assessment and prediction for products for various purposes, including automotive
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A systematic review of software development cost estimation studies
This paper aims to provide a basis for the improvement of software estimation research through a systematic review of previous work. The review identifies 304 software cost estimation papers in 76 journals and classifies the papers according to research topic, estimation approach, research approach, study context and data set. A web-based library of these cost estimation papers is provided to ease the identification of relevant estimation research results. The review results combined with other knowledge provide support for recommendations for future software cost estimation research, including: 1) Increase the breadth of the search for relevant studies, 2) Search manually for relevant papers within a carefully selected set of journals when completeness is essential, 3) Conduct more studies on estimation methods commonly used by the software industry, and, 4) Increase the awareness of how properties of the data sets impact the results when evaluating estimation methods
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