Intelligent Analysis of Software Maintenance Data

2011 ◽  
pp. 14-51
Author(s):  
Marek Reformat ◽  
Petr Musilek ◽  
Efe Igbide
Keyword(s):  
Software Engineering ◽  
Software Maintenance ◽  
Evidence Theory ◽  
Data Models ◽  
Ensemble Prediction ◽  
Software Companies ◽  
Single Defect ◽  
Engineering Data ◽  
Single Data ◽  
Prediction Systems

Amount of software engineering data gathered by software companies amplifies importance of tools and techniques dedicated to processing and analysis of data. More and more methods are being developed to extract knowledge from data and build data models. In such cases, selection of the most suitable data processing methods and quality of extracted knowledge is of great importance. Software maintenance is one of the most time and effort-consuming tasks among all phases of a software life cycle. Maintenance managers and personnel look for methods and tools supporting analysis of software maintenance data in order to gain knowledge needed to prepare better plans and schedules of software maintenance activities. Software engineering data models should provide quantitative as well as qualitative outputs. It is desirable to build these models based on a well-delineated logic structure. Such models would enhance maintainers’ understanding of factors which influence maintenance efforts. This chapter focuses on defect-related activities that are the core of corrective maintenance. Two aspects of these activities are considered: a number of software components that have to be examined during a defect removing process, and time needed to remove a single defect. Analysis of the available datasets leads to development of data models, extraction of IF-THEN rules from these models, and construction of ensemble-based prediction systems that are built based on these data models. The data models are developed using well-known tools such as See5/C5.0 and 4cRuleBuilder, and a new multi-level evolutionary-based algorithm. Single data models are put together into ensemble prediction systems that use elements of evidence theory for the purpose of inference about a degree of belief in the final prediction.

Intelligent Analysis of Software Maintenance Data

2009 ◽  
pp. 189-221
Author(s):  
Marek Reformat ◽  
Petr Musilek ◽  
Efe Igbide
Keyword(s):  
Software Engineering ◽  
Software Maintenance ◽  
Evidence Theory ◽  
Data Models ◽  
Ensemble Prediction ◽  
Software Companies ◽  
Single Defect ◽  
Engineering Data ◽  
Single Data ◽  
Prediction Systems

Amount of software engineering data gathered by software companies amplifies importance of tools and techniques dedicated to processing and analysis of data. More and more methods are being developed to extract knowledge from data and build data models. In such cases, selection of the most suitable data processing methods and quality of extracted knowledge is of great importance. Software maintenance is one of the most time and effort-consuming tasks among all phases of a software life cycle. Maintenance managers and personnel look for methods and tools supporting analysis of software maintenance data in order to gain knowledge needed to prepare better plans and schedules of software maintenance activities. Software engineering data models should provide quantitative as well as qualitative outputs. It is desirable to build these models based on a welldelineated logic structure. Such models would enhance maintainers’ understanding of factors which influence maintenance efforts. This chapter focuses on defect-related activities that are the core of corrective maintenance. Two aspects of these activities are considered: a number of software components that have to be examined during a defect removing process, and time needed to remove a single defect. Analysis of the available datasets leads to development of data models, extraction of IF-THEN rules from these models, and construction of ensemble-based prediction systems that are built based on these data models. The data models are developed using well-known tools such as See5/C5.0 and 4cRuleBuilder, and a new multi-level evolutionary-based algorithm. Single data models are put together into ensemble prediction systems that use elements of evidence theory for the purpose of inference about a degree of belief in the final prediction.

Towards improving statistical modeling of software engineering data: think locally, act globally!

2014 ◽  
Vol 20 (2) ◽  
pp. 294-335 ◽  
Author(s):  
Nicolas Bettenburg ◽  
Meiyappan Nagappan ◽  
Ahmed E. Hassan
Keyword(s):  
Software Engineering ◽  
Statistical Modeling ◽  
Engineering Data

scholarly journals An approach for tuning ensemble prediction systems

2013 ◽  
Vol 65 (1) ◽  
pp. 20594 ◽  
Author(s):  
Antti Solonen ◽  
Heikki Järvinen
Keyword(s):  
Ensemble Prediction ◽  
Prediction Systems

Ontology Augmented Software Engineering

2013 ◽  
pp. 406-413
Author(s):  
Qazi Mudassar Ilyas
Keyword(s):  
Software Engineering ◽  
Semantic Web ◽  
System Integration ◽  
Software Maintenance ◽  
Domain Knowledge ◽  
System Development ◽  
Tracking System ◽  
Software Artifacts ◽  
Natural Choice ◽  
Development Life Cycle

Semantic Web was proposed to make the content machine-understandable by developing ontologies to capture domain knowledge and annotating content with this domain knowledge. Although, the original idea of semantic web was to make content on the World Wide Web machine-understandable, with recent advancements and awareness about these technologies, researchers have applied ontologies in many interesting domains. Many phases in software engineering are dependent on availability of knowledge, and the use of ontologies to capture and process this knowledge is a natural choice. This chapter discusses how ontologies can be used in various stages of the system development life cycle. Ontologies can be used to support requirements engineering phase in identifying and fixing inconsistent, incomplete, and ambiguous requirement. They can also be used to model the requirements and assist in requirements management and validation. During software design and development stages, ontologies can help software engineers in finding suitable components, managing documentation of APIs, and coding support. Ontologies can help in system integration and evolution process by aligning various databases with the help of ontologies capturing knowledge about database schema and aligning them with concepts in ontology. Ontologies can also be used in software maintenance by developing a bug tracking system based upon ontological knowledge of software artifacts and roles of developers involved in software maintenance task.

Environment for Managing Software Maintenance Projects

2011 ◽  
pp. 255-291 ◽  
Author(s):  
Francisco Ruiz ◽  
Felix Garcia ◽  
Mario Piattini ◽  
Macario Polo
Keyword(s):  
Software Engineering ◽  
Software Maintenance ◽  
Business Processes ◽  
Software Tools ◽  
Main Attention ◽  
Process Enactment ◽  
Engineering Environment ◽  
Meta Object Facility ◽  
Wide Perspective ◽  
Main Components

A Software Engineering Environment (SEE) is quite useful in order to manage the complexity of SM projects, since it can provide the needed services. Of the different aspects to highlight in these environments, in this chapter we put our main attention on those that are more directly related to the goal of helping in the management of SM complexity: to approach the SMP from a wide perspective of business processes to integrate technological and management aspects; to define a Process-centered Software Engineering Environment (PSEE); and to use a multilevel conceptual architecture based on standards like MOF (Meta-Object Facility). The MANTIS proposal of integral environment for the management of SM projects is also presented, and the main components of this environment are commented: conceptual tools (multilevel architecture, ontologies, software processes models and metamodels); methodological tools (methodology, and interfaces with organizational and managerial processes) and technical tools (horizontal and vertical software tools, repository, and interaction with process enactment software tools).

Application of Software Metrics in EPR Projects

2011 ◽  
pp. 1172-1181
Author(s):  
S. Parthasarathy
Keyword(s):  
Software Engineering ◽  
Enterprise Resource Planning ◽  
Software Metrics ◽  
Resource Planning ◽  
Erp Implementation ◽  
Complex Products ◽  
Software Companies ◽  
Business Information ◽  
The Poor ◽  
Software Processes

Business information system is an area of the greatest significance in any business enterprise today. Enterprise Resource Planning (ERP) projects are a growing segment of this vital area. Software engineering metrics are units of measurement used to characterize the software engineering products and processes. The research about the software process has acquired great importance in the last few years due to the growing interest of software companies in the improvement of their quality. Enterprise Resource Planning (ERP) projects are very complex products, and this fact is directly linked to their development and maintenance. One of the major reasons found in the literature for the failure of ERP projects is the poor management of software processes. In this chapter, the authors propose a Software Metrics Plan (SMP) containing different software metrics to manage software processes during ERP implementation. Two hypotheses have been formulated and tested using statistical techniques to validate the SMP. The statistical analysis of the collected data from an ERP project supports the two hypotheses, leading to the conclusion that the software metrics are momentous in ERP projects.

scholarly journals Evaluation of two hydrometeorological ensemble strategies for flash-flood forecasting over a catchment of the eastern Pyrenees

2020 ◽  
Vol 20 (2) ◽  
pp. 425-450 ◽  
Author(s):  
Hélène Roux ◽  
Arnau Amengual ◽  
Romu Romero ◽  
Ernest Bladé ◽  
Marcos Sanz-Ramos
Keyword(s):  
Mesoscale Model ◽  
Flash Flood ◽  
Ensemble Prediction ◽  
Flood Warning ◽  
Eastern Pyrenees ◽  
Streamflow Forecasts ◽  
Prediction Systems ◽  
Rainfall Runoff Model ◽  
Alert Threshold ◽  
Threshold Exceedance

Abstract. This study aims at evaluating the performances of flash-flood forecasts issued from deterministic and ensemble meteorological prognostic systems. The hydrometeorological modeling chain includes the Weather Research and Forecasting Model (WRF) forcing the rainfall-runoff model MARINE dedicated to flash floods. Two distinct ensemble prediction systems accounting for (i) perturbed initial and lateral boundary conditions of the meteorological state and (ii) mesoscale model physical parameterizations have been implemented on the Agly catchment of the eastern Pyrenees with three subcatchments exhibiting different rainfall regimes. Different evaluations of the performance of the hydrometeorological strategies have been performed: (i) verification of short-range ensemble prediction systems and corresponding streamflow forecasts, for a better understanding of how forecasts behave; (ii) usual measures derived from a contingency table approach, to test an alert threshold exceedance; and (iii) overall evaluation of the hydrometeorological chain using the continuous rank probability score, for a general quantification of the ensemble performances. Results show that the overall discharge forecast is improved by both ensemble strategies with respect to the deterministic forecast. Threshold exceedance detections for flood warning also benefit from large hydrometeorological ensemble spread. There are no substantial differences between both ensemble strategies on these test cases in terms of both the issuance of flood warnings and the overall performances, suggesting that both sources of external-scale uncertainty are important to take into account.

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