Function Point Analysis for Software Maintenance

Hira, Anandi; Boehm, Barry

doi:10.1145/2961111.2962613

Cited by 17 publications

(6 citation statements)

References 20 publications

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“…A reason to retrospective make a function point analysis in this case was the author's lack of luck to debug and support the site after the creator passed away and the vendor upgraded the PHP-version, rendering the site down. This lack of skills can be attributed in some part to Fredric Brooks -'The mythical man month' [5] but also the teachings of Peter Naur, 'Computing a human activity' and 'Programming as theory building' [15][16][17]. In the section 'program life, death and revival' ties well into the problem not having access to programmers with working knowledge [16].…”

Section: Discussionmentioning

confidence: 99%

“…General system characteristics (GSC) were evaluated for their degree of influence (DOI) summing to the total degrees of influence (TDI): data communication; distributed data processing; performance; heavily used configuration; transaction rate; on-line data entry; end-user efficiency; online update; complex processing; reusability; installation ease; operational ease; multiple sites; and facilitate change. Each GSC was rated for degree of influence (DOI) on a scale from 0 to 5: not present or no influence (0); incidental influence (1); moderate influence (2); average influence (3); significant influence (4); and strong influence (5). The value adjustment factor (VAF) was calculated as 0.01 times TDI + 0.65.…”

Section: External Output (Eo)mentioning

confidence: 99%

“…Function point analysis is attributed to Allan Albright in 1979 [1] and JE Gaffney [2] and further developed in the MK2 report [3]. More recent work on function point analysis, a software tool (Unified code count (UCC)) [4,5]. Function point analysis can be evaluated using UML [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Function Point Analysis FPA on A Team Planning Website Based on PHP and MYSQL

Anders

2018

J Inform Tech Softw Eng

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Section: Discussionmentioning

confidence: 99%

Section: External Output (Eo)mentioning

confidence: 99%

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Function Point Analysis FPA on A Team Planning Website Based on PHP and MYSQL

Anders

2018

J Inform Tech Softw Eng

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“…Moreover, the authors of several articles focused only on the selected group or type of software metrics, limiting considerations to e.g. analysis of the software maintainability (Hira and Boehm 2016), or software cost estimation (Menzies et al 2017).…”

Section: Related Workmentioning

confidence: 99%

Q-Rapids framework for advanced data analysis to improve rapid software development

Kozik

Choraś

Puchalski

et al. 2018

J Ambient Intell Human Comput

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The quality of software, in particular developed rapidly, is quite a challenge for businesses and IT-dependent societies. Therefore, the H2020 Q-Rapids project consortium develops processes and tools to meet this challenge and improve the quality of the software to meet end-users requirements and needs. In this paper, we focus on data analytics that helps software development companies evaluate the quality of the software. In fact, most software development teams use tools such as GitLab, SonarQube or JIRA (among others) to assess the basic characteristics and metrics of the developed software. In this paper, we propose the framework that gathers basic data from the mentioned tools, and processes the data further (e.g. using Apache Kafka, Kibana and Spark) to calculate more advanced metrics, product factors, indicators, and to find correlations between them. In this paper, we present the concept, the technical details, and the initial results of the advanced data analysis methodology. Furthermore, we provide discussion on how to use the system and show the future development directions. We have already implemented the system at software development SME and managed to find interesting characteristics and correlations about the software quality. The results, based on the real data, were interesting to the company product owners and team leaders, and more importantly helped them improve the software quality development process.

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“…There are two types of effort estimation models which are widely used: (i) algorithmic-based models and (ii) non-algorithmic-based models. Some of the most common algorithmic-based models are: COCOMO II [13], Function Point Analysis [14] and Use-Case Points [15]. Whereas, some of the non-algorithmic-based models are: Expert Judgement [16], Analogy Based Estimation [17] and Delphi [18].…”

Section: Introductionmentioning

confidence: 99%

A Novel Effort Estimation Model for Software Requirement Changes During Software Development Phase

Shah¹,

Kama²,

Bakar³

2018

IJSEA

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Software Requirements Changes is a typical phenomenon in any software development project. Restricting incoming changes might cause user dissatisfaction and allowing too many changes might cause delay in project delivery. Moreover, the acceptance or rejection of the change requests become challenging for software project managers when these changes are occurred in Software Development Phase. Where in Software Development Phase software artifacts are not in consistent state such as: some of the class artifacts are Fully Developed, some are Half Developed, some are Major Developed, some are Minor Developed and some are Not Developed yet. However, software effort estimation and change impact analysis are the two most common techniques which might help software project managers in accepting or rejecting change requests during Software Development Phase. The aim of this research is to develop a new software change effort estimation model which helps software project manager in estimating the effort for software Requirement Changes during Software Development Phase. Thus, this research has analyzed the existing effort estimation models and change impact analysis techniques for Softwrae Development Phase from the literature and proposed a new software change effort estimation model by combining change impact analysis technique with effort estimation model. Later, the new proposed model has been evaluated by selecting four small size software projects as case selections in applying experimental approch. The experiment results show that the overall Mean Magnitude Relative Error value produced by the new proposed model is under 25%. Hence it is concluded that the new proposed model is applicable in estimating the amount of effort for requirement changes during SDP.

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Function Point Analysis for Software Maintenance

Cited by 17 publications

References 20 publications

Function Point Analysis FPA on A Team Planning Website Based on PHP and MYSQL

Function Point Analysis FPA on A Team Planning Website Based on PHP and MYSQL

Q-Rapids framework for advanced data analysis to improve rapid software development

A Novel Effort Estimation Model for Software Requirement Changes During Software Development Phase

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