Relevant bibliographies by topics / Software maintenance

Academic literature on the topic 'Software maintenance'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Software maintenance":

1

Parikh, Girish. "Software maintenance." ACM SIGSOFT Software Engineering Notes 10, no. 5 (October 1985): 89–98. http://dx.doi.org/10.1145/382288.382774.

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Leach, Ronald J. "Software metrics and software maintenance." Journal of Software Maintenance: Research and Practice 2, no. 2 (June 1990): 133–42. http://dx.doi.org/10.1002/smr.4360020204.

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Parikh, G. "Software maintenance notes." ACM SIGSOFT Software Engineering Notes 12, no. 2 (April 1987): 51–53. http://dx.doi.org/10.1145/24562.24571.

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Parikh, Girish. "Software maintenance news." ACM SIGSOFT Software Engineering Notes 10, no. 2 (April 1985): 58–59. http://dx.doi.org/10.1145/1012621.1012632.

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Herrin, William R. "Software maintenance costs." ACM SIGCSE Bulletin 17, no. 1 (March 1985): 233–37. http://dx.doi.org/10.1145/323275.323383.

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Seaman, Carolyn B. "Practical Software Maintenance." Journal of Software Maintenance: Research and Practice 12, no. 4 (2000): 249–53. http://dx.doi.org/10.1002/1096-908x(200007/08)12:4<249::aid-smr213>3.0.co;2-n.

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April, Alain, Jane Huffman Hayes, Alain Abran, and Reiner Dumke. "Software Maintenance Maturity Model (SMmm): the software maintenance process model." Journal of Software Maintenance and Evolution: Research and Practice 17, no. 3 (2005): 197–223. http://dx.doi.org/10.1002/smr.311.

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Wee, Nam-Sook. "Optimal Maintenance Schedules of Computer Software." Probability in the Engineering and Informational Sciences 4, no. 2 (April 1990): 243–55. http://dx.doi.org/10.1017/s026996480000156x.

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We present a decision procedure to determine the optimal maintenance intervals of a computer software throughout its operational phase. Our model accounts for the average cost per each maintenance activity and the damage cost per failure with the future cost discounted. Our decision policy is optimal in the sense that it minimizes the expected total cost. Our model assumes that the total number of errors in the software has a Poisson distribution with known mean λ and each error causes failures independently of other errors at a known constant failure rate. We study the structures of the optimal policy in terms of λ and present efficient numerical algorithms to compute the optimal maintenance time intervals, the optimal total number of maintenances, and the minimal total expected cost throughout the maintenance phase.
9

Bhatt, Pankaj, Gautam Shroff, and Arun K. Misra. "Dynamics of software maintenance." ACM SIGSOFT Software Engineering Notes 29, no. 5 (September 2004): 1–5. http://dx.doi.org/10.1145/1022494.1022513.

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Bendifallah, S. "Understanding Software Maintenance Work." IEEE Transactions on Software Engineering SE-13, no. 3 (March 1987): 311–23. http://dx.doi.org/10.1109/tse.1987.233162.

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Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Software maintenance":

1

Taylor, Mark John. "Methodologies and software maintenance." Thesis, University of Salford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265393.

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Sayyad, Shirabad Jelber. "Supporting software maintenance by mining software update records." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/29004.

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It is well known that maintenance is the most expensive stage of the software life cycle. Most large real world software systems consist of a very large number of source code files. Important knowledge about different aspects of a software system is embedded in a rich set of implicit relationships among these files. Those relationships are partly reflected in system documentation at its different levels, but more often than not are never made explicit and become part of the expertise of system maintainers. Finding existing relations between source code components is a difficult task, especially in the case of legacy systems. When a maintenance programmer is looking at a piece of code in a source file, one of the important questions that he or she needs to answer is: "which other files should I know about, i.e. what else might be relevant to this piece of code?". This is an example of a more general Relevance Relation that maps a set of entities in a software system into a relevance value. How can we discover and render explicit these relationships without looking over the shoulder of a programmer involved in a maintenance task? We turn to inductive methods that are capable of extracting structural patterns or models from data. They can learn concepts or models from experience observed in the past to predict outcomes of future unseen cases. This thesis lies at the intersection of two research fields, which has been widely ignored by researchers in the machine learning and software engineering communities. It investigates the application of inductive methods to daily software maintenance at the source code level. Therefore in this thesis we first lay out the general idea of relevance among different entities in a software system. Then using inductive learning methods and a variety of data sources used by maintenance programmers, we extract (i.e. learn) what we call a maintenance relevance relation among files in a large legacy system. In effect we learn from past maintenance experience in the form of problem reports and update records, to be able to make predictions that are useful in future maintenance activities. This relation, which is called the Co-update relation, predicts whether updating one source file may require a change in another file. To learn the Co-update relation we have performed a large number of experiments using syntactic features such as function calls or variable definitions. We have also performed experiments that use text based features such as source code comments and problem reports, and the combination of these features. The results obtained show that while using syntactic features is encouraging in terms of the predictive power of the results of learning, using text based features yields highly accurate models, with precision and recall measures that make these models viable to be used in a real world setting. As part of the contribution of this thesis we also report on challenges encountered in the process and the lessons learned.
3

Breidenbach, Jeff. "Survivable software distribution and maintenance." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/42793.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
Includes bibliographical references (leaves 79-82).
by Jeff Breidenbach.
Ph.D.
4

Foster, John R. "Cost factors in software maintenance." Thesis, Durham University, 1993. http://etheses.dur.ac.uk/1561/.

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Morrison, David J. "Prediction of software maintenance costs." Thesis, Edinburgh Napier University, 2001. http://researchrepository.napier.ac.uk/Output/3601.

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This thesis is concerned with predicting the costs of maintaining a computer program prior to the software being developed. The ubiquitous nature of software means that software maintenance is an important activity, and evidence exists to support the contention that it is the largest and most costly area of endeavour within the software domain. Given the levels of expenditure associated with software maintenance, an ability to quantify future costs and address the determinants of these costs can assist in the planning and allocation of resources. Despite the importance of this field only a limited understanding of the factors that determine future maintenance costs exists, and maintenance estimation is more frequently applied to existing software. A hypothesis has been postulated that suggests the inherent maintainability of the software, the scale of the activity and the degree of change that pertains will determine future software maintenance costs. The variables that contribute to the maintainability of the software have been explored through a survey of past projects, which was undertaken using a questionnaire. This was designed with assistance from three separate teams of professional software engineers. The questionnaire requires 69 numerical or ordinal responses to a series of questions pertaining to characteristics including program structure, computer architecture, software development methodology, project management processes and maintenance outcomes. Factor analysis methods were applied and five of the most powerful predictors are identified. A linear model capable of predicting maintainability has been developed. Validation was undertaken through a series of follow-up interviews with several survey respondents, and by further statistical analysis utilising hold-out samples and structural equation modelling. The model was subsequently used to develop predictive tools intended to provide management support by both providing a categorical assessment of future maintainability, and a quantitative estimate of probable maintenance costs. The distinction between essential corrective maintenance, and other elective forms of maintenance is considered. Conclusions are drawn regarding the efficacy and limitations of tools that can be developedt o supportm anagemendt ecisionm aking. Subjectt o further work with a largers ampleo f projects,p referablyf rom within a singleo rganisationi,t is concluded i that useful tools could be developed to make both categorical ('acceptable' versus 'not acceptable') and static (initial) quantitative predictions. The latter is dependent on the availability of a software development estimate. Some useful predictive methods have also been applied to dynamic (continuing) quantitative prediction in circumstances where a trend develops in successive forecasts. Recommendationfosr furtherw ork arep rovided.T hesei nclude: U Factor analysis and linear regression has been applied to a sample of past software projects from a variety of application areas to identify important input variables for use in a maintainability prediction model. Maintainability is regarded as an important determinant of maintenance resource requirements. The performance of these variables within a single organisation should be confirmed by undertaking a further factor analysis and linear regression on projects from within the target organisation. u The robustness of model design within this target organisation should be considered by applying a sensitivity analysis to the input variables. u This single organisation maintainability predictor model design should be validated by confirmatory interviews with specialists and users from within the target organisation. u Aggregate scale has been identified as another predictor of overall maintenance resource requirements, and the relationship between development and maintenance effort explored for the general case. It is desirable that development and corrective maintenance scale relationships should be explored within a single organisation. Within this environment the association between standardised effort and maintainability should be confirmed, and the value of the logistic model as a descriptor of the relationship verified. u The approacht o quantifying non-correctivem aintenanceth at has been outlined requiresf iirther developmentT. he relationshipb etweena nnualc hanget raffic and maintenancec ostss houldb e modelled,a ssuminga prior knowledgeo f the scale and maintainability determinants. uA sensitivity analysis should be applied to the predictive system that has been developed, recognising the potential for error in the values of the input variables that may pertain. uA goal of this further research should be the development of a suite of soft tools, designed to enable the user to develop a software maintenance estimation system.
6

Revelle, Meghan Kathleen. "Supporting feature-level software maintenance." W&M ScholarWorks, 2010. https://scholarworks.wm.edu/etd/1539623567.

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Software maintenance is the process of modifying a software system to fix defects, improve performance, add new functionality, or adapt the system to a new environment. A maintenance task is often initiated by a bug report or a request for new functionality. Bug reports typically describe problems with incorrect behaviors or functionalities. These behaviors or functionalities are known as features. Even in very well-designed systems, the source code that implements features is often not completely modularized. The delocalized nature of features makes maintaining them challenging. Since maintenance tasks are expressed in terms of features, the goal of this dissertation is to support software maintenance at the feature-level. We focus on two tasks in particular: feature location and impact analysis via feature coupling.;Feature location is the process of identifying the source code that implements a feature, and it is an essential first step to any maintenance task. There are many existing techniques for feature location that incorporate various types of analyses such as static, dynamic, and textual. In this dissertation, we recognize the advantages of leveraging several types of analyses and introduce a new approach to feature location based on combining dynamic analysis, textual analysis, and web mining algorithms applied to software. The use of web mining for feature location is a novel contribution, and we show that our new techniques based on web mining are significantly more effective than the current state of the art.;After using feature location to identify a feature's source code, maintenance can be completed on that feature. Impact analysis should then be performed to revalidate the system and determine which other features may have been affected by the modifications. We define three feature coupling metrics that capture the relationship between features based on structural information, textual information, and their combination. Our novel feature coupling metrics can be used for impact analysis to quantify the strength of coupling between pairs of features. We performed three empirical studies on open-source software systems to assess the feature coupling metrics and established three major results. First, there is a moderate to strong statistically significant correlation between feature coupling and faults. Second, feature coupling can be used to correctly determine about half of the other features that would be affected by a change to a given feature. Finally, we found that the metrics align with developers' opinions about pairs of features that are actually coupled.
7

Akhlaq, Usman, and Muhammad Usman Yousaf. "Impact of Software Comprehension in Software Maintenance and Evolution." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2176.

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The need of change is essential for a software system to reside longer in the market. Change implementation is only done through the maintenance and successful software maintenance gives birth to a new software release that is a refined form of the previous one. This phenomenon is known as the evolution of the software. To transfer software from lower to upper or better form, maintainers have to get familiar with the particular aspects of software i.e. source code and documentation. Due to the poor quality of documentation maintainers often have to rely on source code. So, thorough understanding of source code is necessary for effective change implementation. This study explores the code comprehension problems discussed in the literature and prioritizes them according to their severity level given by maintenance personnel in the industry. Along with prioritizing the problems, study also presents the maintenance personnel suggested methodologies for improving code comprehension. Consideration of these suggestions in development might help in shortening the maintenance and evolution time.
Usman Akhlaq Mirpur, Azad Kashmir, Pakistan Muhammad Usman Yousaf Bhimber, Azad Kashmir, Pakistan
8

LUCAS, ANA PAULA LIMA. "SOFTWARE MAINTENANCE MANAGEMENT: A CASE STUDY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30262@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
A organização participante deste trabalho buscou implantar atividades relacionadas à manutenção de software em função de problemas relacionados à grande ocorrência de defeitos, constantes retrabalhos, entre outros. Para suprimir esses problemas, inicialmente, foi elaborado um estudo preliminar do sistema em questão, avaliando o estado atual da manutenção de software. Diante do diagnóstico realizado, ficou evidente a necessidade de mudanças na maneira como eram conduzidas as atividades de manutenção do sistema. Com isso, iniciou-se a busca por alternativas de melhorias com objetivo de reduzir a ocorrência de defeitos e também aumentar a manutenibilidade do sistema. Sabendo quais são os problemas e o que poderia ser feito para melhorar, propôs-se adotar algumas práticas do modelo de maturidade de manutenção de software - SMmm e integrar os conceitos destas práticas em um processo definido e adaptado às necessidades do sistema. Para apoiar essa implementação, a infraestrutura utilizada foi a plataforma Team Foundation Service - TFS que colaborou com a implementação das práticas selecionadas segundo as exigências do modelo SMmm, resultando em um processo definido apoiado pelo TFS que implementa parcialmente o modelo SMmm. Este trabalho apresenta um estudo de caso com o objetivo de avaliar os benefícios proporcionados pela utilização de algumas práticas do modelo SMmm. A avaliação realizada confronta os dados do estudo preliminar com dados coletados após adoção das práticas, os resultados analisados apontaram uma redução significativa da quantidade de defeitos.
The company participating in this work sought to implement activities related to software maintenance due to problems related to the great occurrence of defects, constant rework, among others. To suppress these problems, a preliminary study of the system in question has been elaborated, evaluating the current state of software maintenance. In view of the diagnosis, the necessity of changes became evident concerning the way that the system maintenance activities were conducted. With this, the search for alternatives of improvements began with the objective of reducing the occurrence of defects and also increase the maintainability of the system. Knowing the problems and what could be done to improve; it was proposed to accede some practices of the software maintenance maturity model - SMmm and to integrate the concepts of these practices into a defined process and adapted to the needs of the system. To support this implementation, the infrastructure used was the Team Foundation Service - TFS platform that collaborated with the implementation of the selected practices according to the requirements of the SMmm model, resulting in a defined process supported by the TFS that partially implements the SMmm model. This paper presents a case study with the objective of evaluating the benefits provided by the use of some practices of the SMmm model. The evaluation carried out compared the data from the preliminary study with data collected after adoption of the practices, the analyzed results pointed out a significant reduction in the number of issues.
9

Lohmann, Wolfgang. "On language processors and software maintenance /." Berlin : Logos-Verl, 2009. http://d-nb.info/995106479/04.

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Panichella, Annibale. "Search-Based Software Maintenance and Testing." Doctoral thesis, Universita degli studi di Salerno, 2014. http://hdl.handle.net/10556/1467.

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2012 - 2013
In software engineering there are many expensive tasks that are performed during development and maintenance activities. Therefore, there has been a lot of e ort to try to automate these tasks in order to signi cantly reduce the development and maintenance cost of software, since the automation would require less human resources. One of the most used way to make such an automation is the Search-Based Software Engineering (SBSE), which reformulates traditional software engineering tasks as search problems. In SBSE the set of all candidate solutions to the problem de nes the search space while a tness function di erentiates between candidate solutions providing a guidance to the optimization process. After the reformulation of software engineering tasks as optimization problems, search algorithms are used to solve them. Several search algorithms have been used in literature, such as genetic algorithms, genetic programming, simulated annealing, hill climbing (gradient descent), greedy algorithms, particle swarm and ant colony. This thesis investigates and proposes the usage of search based approaches to reduce the e ort of software maintenance and software testing with particular attention to four main activities: (i) program comprehension; (ii) defect prediction; (iii) test data generation and (iv) test suite optimiza- tion for regression testing. For program comprehension and defect prediction, this thesis provided their rst formulations as optimization problems and then proposed the usage of genetic algorithms to solve them. More precisely, this thesis investigates the peculiarity of source code against textual documents written in natural language and proposes the usage of Genetic Algorithms (GAs) in order to calibrate and assemble IR-techniques for di erent software engineering tasks. This thesis also investigates and proposes the usage of Multi-Objective Genetic Algorithms (MOGAs) in or- der to build multi-objective defect prediction models that allows to identify defect-prone software components by taking into account multiple and practical software engineering criteria. Test data generation and test suite optimization have been extensively investigated as search- based problems in literature . However, despite the huge body of works on search algorithms applied to software testing, both (i) automatic test data generation and (ii) test suite optimization present several limitations and not always produce satisfying results. The success of evolutionary software testing techniques in general, and GAs in particular, depends on several factors. One of these factors is the level of diversity among the individuals in the population, which directly a ects the exploration ability of the search. For example, evolutionary test case generation techniques that employ GAs could be severely a ected by genetic drift, i.e., a loss of diversity between solutions, which lead to a premature convergence of GAs towards some local optima. For these reasons, this thesis investigate the role played by diversity preserving mechanisms on the performance of GAs and proposed a novel diversity mechanism based on Singular Value Decomposition and linear algebra. Then, this mechanism has been integrated within the standard GAs and evaluated for evolutionary test data generation. It has been also integrated within MOGAs and empirically evaluated for regression testing. [edited by author]
XII n.s.
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Books on the topic "Software maintenance":

1

Westlund, Steve. Software maintenance. St. Louis, MO: Washington University in St. Louis, Center for the Study of Data Processing, 1991.

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McCall, James A. Software maintenance management. Washington: National Bureau of Standards, 1985.

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April, Alain, and Alain Abran. Software Maintenance Management. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2008. http://dx.doi.org/10.1002/9780470258033.

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McCall, James A. Software maintenance management. Gaithersburg, MD: U.S. Dept. of Commerce, National Bureau of Standards, 1985.

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D, Banker Rajiv, and Sloan School of Management. Center for Information Systems Research., eds. Software complexity and software maintenance costs. Cambridge, Mass: Center for Information Systems Research, Sloan School of Management, Massachusetts Institute of Technology, 1992.

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D, Banker Rajiv, Sloan School of Management, and International Center for Research on the Management of Technology., eds. Software complexity and software maintenance costs. Cambridge, Mass: The International Center for Research on the Management of Technology, Sloan School of Management, Massachusetts Institute of Technology, 1990.

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Arthur, Lowell Jay. Software evolution: The software maintenance challenge. New York: Wiley, 1988.

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Tripathy, Priyadarshi, and Kshirasagar Naik. Software Evolution and Maintenance. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118964637.

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Reifer, Donald J. Software maintenance success recipes. Boca Raton: Taylor & Francis, 2012.

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West, Richard. Management of software maintenance. London: HMSO, 1993.

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Book chapters on the topic "Software maintenance":

1

Kehoe, Raymond, and Alka Jarvis. "Software Maintenance." In ISO 9000-3, 111–20. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-0725-2_15.

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Sheu, Phillip C. Y. "Software Maintenance." In Software Engineering and Environment, 261–80. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5907-8_10.

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Weik, Martin H. "software maintenance." In Computer Science and Communications Dictionary, 1611. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17660.

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Weik, Martin H. "maintenance software." In Computer Science and Communications Dictionary, 969. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_10975.

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Sherer, Susan A. "Software Maintenance." In Software Failure Risk, 209–19. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3020-6_10.

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Juuso, Ilkka, and Ilpo Pöyhönen. "Software Maintenance." In Medical-Grade Software Development, 175–88. New York: Productivity Press, 2023. http://dx.doi.org/10.4324/b23403-6.

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de Guzmán, Ignacio García-Rodríguez, Mario Piattini, and Ricardo Pérez-Castillo. "Green Software Maintenance." In Green in Software Engineering, 205–29. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08581-4_9.

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Anquetil, Nicolas, Káthia M. de Oliveira, and Márcio G. B. Dias. "Software Maintenance Ontology." In Ontologies for Software Engineering and Software Technology, 153–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-34518-3_5.

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van Vliet, J. C. "Teaching software maintenance." In Software Engineering Education, 80–89. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/bfb0042351.

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Khomh, Foutse, Mohammad Masudur Rahman, and Antoine Barbez. "Intelligent Software Maintenance." In Optimising the Software Development Process with Artificial Intelligence, 241–75. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9948-2_9.

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Conference papers on the topic "Software maintenance":

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Bordin, Andréa Sabedra, and Fabiane Barreto Vavassori Benitti. "Software maintenance." In the XXXII Brazilian Symposium. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3266237.3266251.

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Herrin, William R. "Software maintenance costs." In the sixteenth SIGCSE technical symposium. New York, New York, USA: ACM Press, 1985. http://dx.doi.org/10.1145/323287.323383.

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Lin, I. H., and D. A. Gustafson. "Classifying software maintenance." In 1988 Conference on Software Maintenance. IEEE, 1988. http://dx.doi.org/10.1109/icsm.1988.10169.

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Gallagher, Keith, Mark Fioravanti, and Suzanne Kozaitis. "Teaching Software Maintenance." In 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2019. http://dx.doi.org/10.1109/icsme.2019.00054.

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Ouchi, Miheko L. "Software maintenance documentation." In the 4th annual international conference. New York, New York, USA: ACM Press, 1985. http://dx.doi.org/10.1145/10563.10567.

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Glass, R. "Software maintenance documentation." In the 7th annual international conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/74311.74325.

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Rehman, Fateh ur, Bilal Maqbool, Muhammad Qasim Riaz, Usman Qamar, and Muhammad Abbas. "Scrum Software Maintenance Model: Efficient Software Maintenance in Agile Methodology." In 2018 21st Saudi Computer Society National Computer Conference (NCC). IEEE, 2018. http://dx.doi.org/10.1109/ncg.2018.8593152.

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Karim, Ramin, Mira Kajko-Mattsson, Peter Soderholm, Olov Candell, Tommy Tyrberg, Hans Ohlund, and Jan Johansson. "Positioning embedded software maintenance within industrial maintenance." In 2008 IEEE International Conference on Software Maintenance (ICSM). IEEE, 2008. http://dx.doi.org/10.1109/icsm.2008.4658099.

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Black, Paul E. "Software Assurance During Maintenance." In 22nd IEEE International Conference on Software Maintenance. IEEE, 2006. http://dx.doi.org/10.1109/icsm.2006.58.

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Stancel, Martin, Hana Pacaiova, and Eva Chovancova. "Maintenance performance audit software." In 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2017. http://dx.doi.org/10.1109/sami.2017.7880353.

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Reports on the topic "Software maintenance":

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Osborne, Wilma M. Software maintenance management. Gaithersburg, MD: National Bureau of Standards, 1985. http://dx.doi.org/10.6028/nbs.sp.500-129.

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Osborne, Wilma M. Executive guide to software maintenance. Gaithersburg, MD: National Bureau of Standards, 1985. http://dx.doi.org/10.6028/nbs.sp.500-130.

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Osborne, Wilma M., and Ron Raigrodski. Annotated bibliogrpahy on software maintenance. Gaithersburg, MD: National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.sp.500-141.

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Engle, Charles B., Gary Ford, and Tim Korson. Software Maintenance Exercises for a Software Engineering Project Course. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada235779.

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Landis, L. D., A. J. Fine, A. L. Gilbert, and P. M. Hyland. Automatic Documentation Methodologies for Software Maintenance. Fort Belvoir, VA: Defense Technical Information Center, January 1989. http://dx.doi.org/10.21236/ada204752.

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Schneidewind, Norman F. Software Maintenance: The Need for Standardization. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada205229.

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Landis, L. D., A. J. Fine, W. L. Hembree, A. L. Gilbert, and P. M. Hyland. Documentation in a Software Maintenance Environment. Fort Belvoir, VA: Defense Technical Information Center, August 1987. http://dx.doi.org/10.21236/ada185892.

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Dart, Susan, Alan M. Christie, and Alan W. Brown. A Case Study in Software Maintenance. Fort Belvoir, VA: Defense Technical Information Center, July 1993. http://dx.doi.org/10.21236/ada269923.

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Burnett, Margaret M. Information Foraging Theory in Software Maintenance. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada579505.

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Auerbach, J. OASIS, LLNL version: Software maintenance manual. Office of Scientific and Technical Information (OSTI), March 1990. http://dx.doi.org/10.2172/6876600.

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