Relevant bibliographies by topics / Defective software

Academic literature on the topic 'Defective software'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Defective software"

1

Lloyd, I. "Liability for defective software." Reliability Engineering & System Safety 32, no. 1-2 (January 1991): 193–207. http://dx.doi.org/10.1016/0951-8320(91)90054-b.

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Guzdial, Mark, and Bertrand Meyer. "Understanding CS1 students; defective software." Communications of the ACM 55, no. 1 (January 2012): 14–15. http://dx.doi.org/10.1145/2063176.2063180.

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Hedley, Steve. "Defective Software in the Court of Appeal." Cambridge Law Journal 56, no. 1 (March 1997): 21–25. http://dx.doi.org/10.1017/s000819730001761x.

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Parry, Rex. "Who bears the risk of defective software?" Computer Audit Update 1996, no. 12 (December 1996): 27–29. http://dx.doi.org/10.1016/s0960-2593(97)80803-8.

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Rakitin, S. R. "Coping with Defective Software in Medical Devices." Computer 39, no. 4 (April 2006): 40–45. http://dx.doi.org/10.1109/mc.2006.123.

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Wan, Hongyan, Guoqing Wu, Mali Yu, and Mengting Yuan. "Software Defect Prediction Based on Cost-Sensitive Dictionary Learning." International Journal of Software Engineering and Knowledge Engineering 29, no. 09 (September 2019): 1219–43. http://dx.doi.org/10.1142/s0218194019500384.

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Software defect prediction technology has been widely used in improving the quality of software system. Most real software defect datasets tend to have fewer defective modules than defective-free modules. Highly class-imbalanced data typically make accurate predictions difficult. The imbalanced nature of software defect datasets makes the prediction model classifying a defective module as a defective-free one easily. As there exists the similarity during the different software modules, one module can be represented by the sparse representation coefficients over the pre-defined dictionary which consists of historical software defect datasets. In this study, we make use of dictionary learning method to predict software defect. We optimize the classifier parameters and the dictionary atoms iteratively, to ensure that the extracted features (sparse representation) are optimal for the trained classifier. We prove the optimal condition of the elastic net which is used to solve the sparse coding coefficients and the regularity of the elastic net solution. Due to the reason that the misclassification of defective modules generally incurs much higher cost risk than the misclassification of defective-free ones, we take the different misclassification costs into account, increasing the punishment on misclassification defective modules in the procedure of dictionary learning, making the classification inclining to classify a module as a defective one. Thus, we propose a cost-sensitive software defect prediction method using dictionary learning (CSDL). Experimental results on the 10 class-imbalance datasets of NASA show that our method is more effective than several typical state-of-the-art defect prediction methods.
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Tomar, Divya, and Sonali Agarwal. "Prediction of Defective Software Modules Using Class Imbalance Learning." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/7658207.

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Software defect predictors are useful to maintain the high quality of software products effectively. The early prediction of defective software modules can help the software developers to allocate the available resources to deliver high quality software products. The objective of software defect prediction system is to find as many defective software modules as possible without affecting the overall performance. The learning process of a software defect predictor is difficult due to the imbalanced distribution of software modules between defective and nondefective classes. Misclassification cost of defective software modules generally incurs much higher cost than the misclassification of nondefective one. Therefore, on considering the misclassification cost issue, we have developed a software defect prediction system using Weighted Least Squares Twin Support Vector Machine (WLSTSVM). This system assigns higher misclassification cost to the data samples of defective classes and lower cost to the data samples of nondefective classes. The experiments on eight software defect prediction datasets have proved the validity of the proposed defect prediction system. The significance of the results has been tested via statistical analysis performed by using nonparametric Wilcoxon signed rank test.
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Huhryanskaya, E., and M. Surikova. "Mathematical software for automation systems of defective whips bucking." Актуальные направления научных исследований XXI века: теория и практика 3, no. 2 (May 3, 2015): 364–68. http://dx.doi.org/10.12737/11115.

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Mehrez, Ahmed. "Reassessing Software Quality Performance." International Journal of Knowledge Management 10, no. 1 (January 2014): 58–77. http://dx.doi.org/10.4018/ijkm.2014010104.

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Software quality has always been described as a poorly developed construct. Several reports and much evidence show clear problems related to software quality. This research empirically tests if ineffective implementation of knowledge management activities would be a reason behind possible existence of defective quality performance in the software industry. The main finding shows that knowledge management would directly affect quality performance in the Egyptian software industry. Statistical correlation is significant between the two constructs; knowledge management and quality performance.
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Mishra, Bharavi, and K. K. Shukla. "Mining Attributes Patterns of Defective Modules for Object Oriented Software." International Journal of Computer Applications 54, no. 11 (September 25, 2012): 14–18. http://dx.doi.org/10.5120/8610-2462.

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Dissertations / Theses on the topic "Defective software"

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Marshall, Andrew. "Liability for Defective Software in South Africa." Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/4663.

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"There is no such thing as error-free code." This is neither the favoured mantra of a software development guru, nor the refrain of the chairman of a multinational software company, but the consensus of those at the coalface of software development the world over. [...] This paradigm raises interesting questions for the lawyer. There is no other discipline that operates on this basis. [...] It is only the software developer who, because of the complexity of software code, as well as the unpredictable environment in which his software will be executed, admits that the chance of his creation not performing as intended under all circumstances is so high that he must assume there are faults in it. [...] The distinction in computer operations between hardware and software is well understood, and while I will be examining the liability for defective software, the reader would do well to bear in mind that software is often inextricably bound to the hardware that it serves.
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Vandehei, Bailey R. "Leveraging Defects Life-Cycle for Labeling Defective Classes." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2111.

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Data from software repositories are a very useful asset to building dierent kinds of models and recommender systems aimed to support software developers. Specically, the identication of likely defect-prone les (i.e., classes in Object-Oriented systems) helps in prioritizing, testing, and analysis activities. This work focuses on automated methods for labeling a class in a version as defective or not. The most used methods for automated class labeling belong to the SZZ family and fail in various circum- stances. Thus, recent studies suggest the use of aect version (AV) as provided by developers and available in the issue tracker such as JIRA. However, in many cir- cumstances, the AV might not be used because it is unavailable or inconsistent. The aim of this study is twofold: 1) to measure the AV availability and consistency in open-source projects, 2) to propose, evaluate, and compare to SZZ, a new method for labeling defective classes which is based on the idea that defects have a stable life-cycle in terms of proportion of versions needed to discover the defect and to x the defect. Results related to 212 open-source projects from the Apache ecosystem, featuring a total of about 125,000 defects, show that the AV cannot be used in the majority (51%) of defects. Therefore, it is important to investigate automated meth- ods for labeling defective classes. Results related to 76 open-source projects from the Apache ecosystem, featuring a total of about 6,250,000 classes that are are aected by 60,000 defects and spread over 4,000 versions and 760,000 commits, show that the proposed method for labeling defective classes is, in average among projects and de- fects, more accurate, in terms of Precision, Kappa, F1 and MCC than all previously proposed SZZ methods. Moreover, the improvement in accuracy from combining SZZ with defects life-cycle information is statistically signicant but practically irrelevant ( overall and in average, more accurate via defects' life-cycle than any SZZ method.
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Powell, Daniel, and n/a. "Formal Methods For Verification Based Software Inspection." Griffith University. School of Computing and Information Technology, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030925.154706.

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Useful processes, that are independently repeatable, are utilised in all branches of science and traditional engineering disciplines but seldom in software engineering. This is particularly so with processes used for detection and correction of defects in software systems. Code inspection, as introduced by Michael Fagan at IBM in the mid 1970's is widely recognised as an effective technique for finding defects in software. Despite its reputation, code inspection, as it is currently practiced, is not a strictly repeatable process. This is due to the problems faced by inspectors when they attempt to paraphrase the complicated semantics of a unit of computer code. Verification based software inspection, as advocated by the cleanroom software engineering community, requires that arguments of correctness be formulated with the code and its specification. These arguments rely on the reader being able to extract the semantics from the code. This thesis addresses the requirement for an independently repeatable, scalable and substantially automated method for yielding semantics from computer code in a complete, unambiguous and consistent manner in order to facilitate, and make repeatable, verification based code inspection. Current literature regarding the use of code inspection for verification of software is surveyed. Empirical studies are referenced, comparing inspection to software testing and program proof. Current uses of formal methods in software engineering will be discussed, with particular reference to formal method applications in verification. Forming the basis of the presented method is a systematic, and hence repeatable, approach to the derivation of program semantics. The theories and techniques proposed for deriving semantics from program code extend current algorithmic and heuristic techniques for deriving invariants. Additionally, the techniques introduced yield weaker forms of invariant information which are also useful for verification, defect detection and correction. Methods for using these weaker invariant forms, and tools to support these methods, are introduced. Algorithmic and heuristic techniques for investigating loop progress and termination are also introduced. Some of these techniques have been automated in supporting tools, and hence, the resulting defects can be repeatably identified. Throughout this thesis a strong emphasis is placed on describing implementable algorithms to realise the derivation techniques discussed. A number of these algorithms are implemented in a tool to support the application of the verification methods presented. The techniques and tools presented in this thesis are well suited, but not limited to, supporting rigorous methods of defect detection as well as formal and semi-formal reasoning of correctness. The automation of these techniques in tools to support practical, formal code reading and correctness argument will assist in addressing the needs of trusted component technologies and the general requirement for quality in software.
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Powell, Daniel. "Formal Methods For Verification Based Software Inspection." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366466.

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Useful processes, that are independently repeatable, are utilised in all branches of science and traditional engineering disciplines but seldom in software engineering. This is particularly so with processes used for detection and correction of defects in software systems. Code inspection, as introduced by Michael Fagan at IBM in the mid 1970's is widely recognised as an effective technique for finding defects in software. Despite its reputation, code inspection, as it is currently practiced, is not a strictly repeatable process. This is due to the problems faced by inspectors when they attempt to paraphrase the complicated semantics of a unit of computer code. Verification based software inspection, as advocated by the cleanroom software engineering community, requires that arguments of correctness be formulated with the code and its specification. These arguments rely on the reader being able to extract the semantics from the code. This thesis addresses the requirement for an independently repeatable, scalable and substantially automated method for yielding semantics from computer code in a complete, unambiguous and consistent manner in order to facilitate, and make repeatable, verification based code inspection. Current literature regarding the use of code inspection for verification of software is surveyed. Empirical studies are referenced, comparing inspection to software testing and program proof. Current uses of formal methods in software engineering will be discussed, with particular reference to formal method applications in verification. Forming the basis of the presented method is a systematic, and hence repeatable, approach to the derivation of program semantics. The theories and techniques proposed for deriving semantics from program code extend current algorithmic and heuristic techniques for deriving invariants. Additionally, the techniques introduced yield weaker forms of invariant information which are also useful for verification, defect detection and correction. Methods for using these weaker invariant forms, and tools to support these methods, are introduced. Algorithmic and heuristic techniques for investigating loop progress and termination are also introduced. Some of these techniques have been automated in supporting tools, and hence, the resulting defects can be repeatably identified. Throughout this thesis a strong emphasis is placed on describing implementable algorithms to realise the derivation techniques discussed. A number of these algorithms are implemented in a tool to support the application of the verification methods presented. The techniques and tools presented in this thesis are well suited, but not limited to, supporting rigorous methods of defect detection as well as formal and semi-formal reasoning of correctness. The automation of these techniques in tools to support practical, formal code reading and correctness argument will assist in addressing the needs of trusted component technologies and the general requirement for quality in software.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Computing and Information Technology
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Al-Agealy, Talal Ali Kadhim. "Computer software packages and factor analysis to deal with defective and incomplete demographic data." Thesis, City University London, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.346423.

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Books on the topic "Defective software"

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Girot, Clarisse. User protection in IT contracts: A comparative study of the protection of the user against defective performance in information technology. Boston: Kluwer Law International, 2001.

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Lloyd, Ian J. 24. Contractual liability for defective software. Oxford University Press, 2017. http://dx.doi.org/10.1093/he/9780198787556.003.0024.

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This chapter begins with a discussion of the nature of software defects. It then covers the legal status of software and software contracts; implied terms in software contracts; remedies for breach of the implied terms; the approach adopted by the courts in the limited number of cases which have reached the High Court or Court of Appeal; exclusion or limitation of liability; and the enforceability of shrink-wrap licences.
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Girot, Clarisse. User Protection in IT Contracts: A Comparative Study of the Protection of the User Against Defective Performance in Information Technology. Kluwer Law International, 2002.

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Contract pricing: Subcontractor defective pricing audits : fact sheet for the Chairman, Committee on Governmental Affairs, U.S. Senate. Washington, D.C: The Office, 1991.

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Book chapters on the topic "Defective software"

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Nascimento, Alexandre Moreira, Vinícius Veloso de Melo, Luiz Alberto Vieira Dias, and Adilson Marques da Cunha. "Increasing the Prediction Quality of Software Defective Modules with Automatic Feature Engineering." In Advances in Intelligent Systems and Computing, 527–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77028-4_68.

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Martono, Niken Prasasti, Katsutoshi Kanamori, and Hayato Ohwada. "Utilizing Customers’ Purchase and Contract Renewal Details to Predict Defection in the Cloud Software Industry." In Lecture Notes in Computer Science, 138–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13332-4_12.

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Prasasti, Niken, Masato Okada, Katsutoshi Kanamori, and Hayato Ohwada. "Customer Lifetime Value and Defection Possibility Prediction Model Using Machine Learning: An Application to a Cloud-Based Software Company." In Intelligent Information and Database Systems, 62–71. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05458-2_7.

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Lloyd, Ian J. "24. Contractual liability for defective software." In Information Technology Law, 469–502. Oxford University Press, 2014. http://dx.doi.org/10.1093/he/9780198702320.003.0024.

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"Influence research on file of software defective testing configuration." In Civil, Architecture and Environmental Engineering, 1341–46. CRC Press, 2017. http://dx.doi.org/10.1201/9781315226187-247.

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Salzman, Harold, and Stephen R. Rosenthal. "Keeping the Customer Satisfied: Field Service and the Art of Automation." In Software by Design. Oxford University Press, 1994. http://dx.doi.org/10.1093/oso/9780195083408.003.0011.

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The “Maytag repairman” is the familiar image of field service. When servicing electronic equipment such as computers, however, the job is significantly more demanding than fixing washing machines. Not only are computers more complex than most other machines, they are also more central to the ongoing operations of an organization. Increasingly, everything an organization does depends upon electronic equipment in some way. Large computer systems are often expected to run 24 hours a day, 365 days a year. When computers are such an integral part of an organization’s operations, maintaining the equipment is tantamount to keeping a person’s heart beating and blood circulating. Thus, unlike the idle television caricature, field service engineers are increasingly viewed as the paramedics for electronics who can quickly and ably respond to system crashes. At the same time that field service has become more demanding, the complexity of the job has increased. The “machinery” of the modern organization is less often composed of gears turning, typewriters clattering, and paper being shuffled. Instead, the sights and sounds of the modern organization consist of screens glowing, keys clicking, and, to the uninitiated, an assemblage of opaque, “black boxes.” Inside these boxes is a miniature world that gives no clue as to the nature of its inner workings. The field service engineer’s (FE) job and function have been growing while the size of the technology itself has been shrinking. His or her skill is less often exercised as a skilled craftworker in the repair of a part and more often as a skilled analyst who can understand the abstract workings and nature of electronics to identify the problem and trace it to the malfunctioning component. The required manual skills are often minimal, just enough dexterity to swap out a bad part for a good one usually suffices. At the same time, the scope of field service has expanded from just repairing worn and broken or defective parts to collecting information about design defects and debugging equipment that may have been released prematurely.
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Vossler, Colleen Kotyk, and Jeffrey Voas. "Defective software: An overview of legal remedies and technical measures available to consumers." In Advances in Computers, 451–97. Elsevier, 2000. http://dx.doi.org/10.1016/s0065-2458(00)80010-8.

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Panda, Mrutyunjaya, and Ahmad Taher Azar. "Hybrid Multi-Objective Grey Wolf Search Optimizer and Machine Learning Approach for Software Bug Prediction." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 314–37. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5788-4.ch013.

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Software bugs (or malfunctions) pose a serious threat to software developers with many known and unknown bugs that may be vulnerable to computer systems, demanding new methods, analysis, and techniques for efficient bug detection and repair of new unseen programs at a later stage. This chapter uses evolutionary grey wolf (GW) search optimization as a feature selection technique to improve classifier efficiency. It is also envisaged that software error detection would consider the nature of the error when repairing it for remedial action instead of simply finding it either faulty or non-defective. To address this problem, the authors use bug severity multi-class classification to build an efficient and robust prediction model using multilayer perceptron (MLP), logistic regression (LR), and random forest (RF) for bug severity classification. Both tests are performed on two software error datasets, namely Ant 1.7 and Tomcat.
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Rao, K. Nitalaksheswara. "Improved Hybrid Sampling Strategy for Software Defect Prediction of Imbalanced Data Distribution." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 215–36. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4225-8.ch013.

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Software defect prediction using data mining techniques is one of the best practices for finding defective modules. The existing classification techniques can be used for efficient knowledge discovery on normal datasets. Most of the real-world data sources are biased towards any one of the classes. This type of data source is known as class imbalance or skewed data sources. The defect prediction rate for the class imbalance datasets reduces with the increases in the class imbalance nature. To handle such type of datasets, an approach with specific designing technique is required for improved performance. In this chapter, the authors propose an algorithm known as improved integrated sampling strategy (IISS) for improved performance using noisy removal strategy for software defect prediction. The experimental analysis conducted on skewed software defect prediction datasets provides the results that IISS algorithm have performed well when compared with C4.5, C4.5+Balance, RF, and RF+Balance algorithms with various class imbalance evaluation measures.
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Sprague, Robert D. "Liability for System and Data Quality." In Social, Ethical and Policy Implications of Information Technology, 183–203. IGI Global, 2004. http://dx.doi.org/10.4018/978-1-59140-168-1.ch011.

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This chapter discusses various theories of legal liability related to computer system and data quality. Contract-based theories are discussed in detail, as most computer systems are acquired and data are accessed through some form of contractual relationship. Additional tort-based theories of liability are also discussed, particularly relating to publication of inaccurate data. As presented in this chapter, purchasers of defective computer hardware or software and users of inaccurate data have very limited legal remedies available. Further, the legal remedies that may be available are typically severely restricted by the contract through which the computer system is acquired or the data are accessed.
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Conference papers on the topic "Defective software"

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Blanks, Michael. "Why we tolerate defective software." In the 22nd annual ACM computer science conference. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/197530.197636.

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Zhang, Hongyu, Xiuzhen Zhang, and Ming Gu. "Predicting Defective Software Components from Code Complexity Measures." In 13th Pacific Rim International Symposium on Dependable Computing (PRDC 2007). IEEE, 2007. http://dx.doi.org/10.1109/prdc.2007.28.

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Gozuacik, Necip, and ve Altan Parlak. "A Virtual Assistant for Predicting Defective Software Module." In 2021 29th Signal Processing and Communications Applications Conference (SIU). IEEE, 2021. http://dx.doi.org/10.1109/siu53274.2021.9477786.

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"Using Models to Assess Impact of Defective Software." In 9th International Workshop on Modelling, Simulation, Verification and Validation of Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003596000710077.

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Ibrahim, A. Al, C. Chauviere, and P. Bonnet. "Software Correction of Defective Lossy Transmission Line Networks." In 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring). IEEE, 2019. http://dx.doi.org/10.1109/piers-spring46901.2019.9017375.

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Yu, Yangxin, and Liuyang Wang. "Influence research on file of software defective testing configuration." In Proceedings of the International Conference on Civil, Architecture and Environmental Engineering (ICCAE2016). CRC Press/Balkema P.O. Box 11320, 2301 EH Leiden, The Netherlands: CRC Press/Balkema, 2017. http://dx.doi.org/10.1201/9781315116242-49.

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Rahman, Akond. "Characteristics of defective infrastructure as code scripts in DevOps." In ICSE '18: 40th International Conference on Software Engineering. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3183440.3183452.

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Petrić, Jean, Tracy Hall, and David Bowes. "How Effectively Is Defective Code Actually Tested?" In PROMISE'18: The 14th International Conference on Predictive Models and Data Analytics in Software Engineering. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3273934.3273939.

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Rahman, Akond, and Laurie Williams. "Characterizing Defective Configuration Scripts Used for Continuous Deployment." In 2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST). IEEE, 2018. http://dx.doi.org/10.1109/icst.2018.00014.

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An, Do Gyu, Jung Won Jung, and Jae Wook Jeon. "Defective Fiducial Mark Detection Using Machine Learning." In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2018. http://dx.doi.org/10.1109/snpd.2018.8441132.

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