25,804 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
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
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
Methods of Technical Prognostics Applicable to Embedded Systems
Hlavní cílem dizertace je poskytnutí uceleného pohledu na problematiku technické prognostiky, která nachází uplatnění v tzv. prediktivní údržbě založené na trvalém monitorování zařízení a odhadu úrovně degradace systému či jeho zbývající životnosti a to zejména v oblasti komplexních zařízení a strojů. V současnosti je technická diagnostika poměrně dobře zmapovaná a reálně nasazená na rozdíl od technické prognostiky, která je stále rozvíjejícím se oborem, který ovšem postrádá větší množství reálných aplikaci a navíc ne všechny metody jsou dostatečně přesné a aplikovatelné pro embedded systémy. Dizertační práce přináší přehled základních metod použitelných pro účely predikce zbývající užitné životnosti, jsou zde popsány metriky pomocí, kterých je možné jednotlivé přístupy porovnávat ať už z pohledu přesnosti, ale také i z pohledu výpočetní náročnosti. Jedno z dizertačních jader tvoří doporučení a postup pro výběr vhodné prognostické metody s ohledem na prognostická kritéria. Dalším dizertačním jádrem je představení tzv. částicového filtrovaní (particle filtering) vhodné pro model-based prognostiku s ověřením jejich implementace a porovnáním. Hlavní dizertační jádro reprezentuje případovou studii pro velmi aktuální téma prognostiky Li-Ion baterii s ohledem na trvalé monitorování. Případová studie demonstruje proces prognostiky založené na modelu a srovnává možné přístupy jednak pro odhad doby před vybitím baterie, ale také sleduje možné vlivy na degradaci baterie. Součástí práce je základní ověření modelu Li-Ion baterie a návrh prognostického procesu.The main aim of the thesis is to provide a comprehensive overview of technical prognosis, which is applied in the condition based maintenance, based on continuous device monitoring and remaining useful life estimation, especially in the field of complex equipment and machinery. Nowadays technical prognosis is still evolving discipline with limited number of real applications and is not so well developed as technical diagnostics, which is fairly well mapped and deployed in real systems. Thesis provides an overview of basic methods applicable for prediction of remaining useful life, metrics, which can help to compare the different approaches both in terms of accuracy and in terms of computational/deployment cost. One of the research cores consists of recommendations and guide for selecting the appropriate forecasting method with regard to the prognostic criteria. Second thesis research core provides description and applicability of particle filtering framework suitable for model-based forecasting. Verification of their implementation and comparison is provided. The main research topic of the thesis provides a case study for a very actual Li-Ion battery health monitoring and prognostics with respect to continuous monitoring. The case study demonstrates the prognostic process based on the model and compares the possible approaches for estimating both the runtime and capacity fade. Proposed methodology is verified on real measured data.
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