The Constructive Cost Model (COCOMO) is an algorithmic software cost estimation model developed by Barry Boehm. The model uses a basic regression formula, with parameters that are derived from historical project data and current project characteristics.
Basic COCOMO compute software development effort (and cost) as a function of program size. Program size is expressed in estimated thousands of source lines of code (SLOC, KLOC).
COCOMO Model
Key parameters which define the quality of any software
Modes of development
Boehm’s definition of systems
Types of Models
Advantages
disadvantages
The Constructive Cost Model (COCOMO) is an algorithmic software cost estimation model developed by Barry Boehm. The model uses a basic regression formula, with parameters that are derived from historical project data and current project characteristics.
Basic COCOMO compute software development effort (and cost) as a function of program size. Program size is expressed in estimated thousands of source lines of code (SLOC, KLOC).
COCOMO Model
Key parameters which define the quality of any software
Modes of development
Boehm’s definition of systems
Types of Models
Advantages
disadvantages
This presentation describes:
- What is software size?
- How to Measure Software size?
- Techniques and parameters in Software Size estimation
- Where and how to apply the techniques?
This presentation describes:
- What is software size?
- How to Measure Software size?
- Techniques and parameters in Software Size estimation
- Where and how to apply the techniques?
This ppt presentation is based on the Cost Estimation Model of software engineering. This is used to estimate the cost required to develop the project.
COCOMO stands for COnstructive COst estimation MOdel.
The costs are estimated when the whole software project planning is done after the feasibility study phase of any software development model.
COCOMO is the most important stage of the Software Project Management.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
2. Table Of Content
Introduction to COCOMO Model
Key Parameters for testing Quality
Development Modes
Comparison of Three COCOMO modes
COCOMO Models
Advantages and Disadvantages
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3. Introduction to COCOMO Model
The COCOMO model is one of the most popular cost estimating model in
software engineering domain which is based upon LOC (Line Of Code).
COCOMO stands for Constructive Cost Model.
It is a procedural cost estimate model for software projects.
It was proposed by Barry Boehm in 1970 and published in 1981 .
COCOMO predicts the effort and schedule for a software product
development based on inputs related to the size of the software.
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4. Key Parameters for testing Quality
Effort & Schedule
Effort: Amount of labor that will be required to complete a
task. It is measured in person-months units.
Schedule: Amount of time that will be required for the
completion of the job. It is measured in the units of time such
as weeks, months.
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5. Development Modes
There are three modes of software development project based on development
complexity.
.Organic Mode
Semidetached Mode
Embedded Mode
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6. Organic Mode
Relatively small, simple software projects .
problem is well understood and has been solved in the past.
Relatively small and requires little innovation.
Team size is small and team members have a good experience.
Semi-detached Mode
The projects classified as Semi-Detached are comparatively less familiar and difficult to
develop compared to the organic ones.
Require better guidance and creativity.
Team size is medium with mixed experience.
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7. Embedded Mode
It is combination of organic and semi detach mode.
A software project with requiring the highest level of complexity, creativity, and
experience requirement fall under this category.
Such software requires a larger team size than the other two mode.
Developers need to be sufficiently experienced and creative to develop such
complex models.
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10. Basic COCOMO Model
Basic COCOMO is good for quick, early, rough of software costs.
It does not account for differences in hardware constraints, Team quality and
experience, use of modern tools and techniques, and other project attributes.
Project estimations:
Approximate estimate of project parameters(cost , development Time, persons
required to complete a task).
Software development effort is estimated using LOC(Line Of Code)
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11. Basic COCOMO Model: Equations
(Effort applied)E=ab (KLOC) b
b
(Development Time)D=cb (E) d
b
(Productivity)P= KLOC/E
(Staff Size)SS = E/D persons
Where
KLOC Kilo Line Of Code(Thousands of line).
A,b,c,d are coefficients.
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12. Example
Suppose that a project was estimated to be 400 KLOC. Calculate the effort and development time for
each of the three modes i.e. organic , semidetached and embedded.
Solution The basic COCOMO equations take the form: E = ab (KLOC)bb
D = cb (E)db
Estimated size of the project = 400 KLOC
1. Organic Mode
E = 2.4 (400)1.05 = 1295.31
D = 2.5 (1295.31)0.38 = 38.07
2. Semi detached Mode
E = 3.0 (400)1.12 = 2462.79
D = 2.5 (2462.79)0.35 = 38.45
3. Embedded Mode
E = 3.6 (400)1.20 = 4772.81
D = 2.5 (4772.81)0.32 = 37.59
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13. Intermediate COCOMO Model
It is extension of Basic COCOMO model. In the Intermediate model ,additional
set of 15 predictors called cost drivers are introduced.
It refine estimates obtained by Basic COCOMO model.
It refines cost estimations using 15 cost drivers.
Classification of Cost Drivers and their attributes
The cost drivers are grouped into 4 categories:-
1.Product attributes
a. Required software reliability (RELY)
b. Database size (DATA)
c. Product complexity (CPLX)
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14. Classification of Cost Drivers and their attributes(cont’d)
2. Computer attributes
a. Execution time constraint (TIME)
b. Main store constraint (STOR)
c. Virtual machine volatility (VIRT)
d. Computer turnaround time (TURN)
3. Personnel attributes
a. Analyst capability (ACAP)
b. Application experience (AEXP)
c. Programmer capability (PCAP)
d. Virtual machine experience (VEXP)
e. Programming Language experience (LEXP)
4. Project attributes
a. Modern programming practices (MODP)
b. Use of software tool (TOOL)
c. Required development schedule (SCED) 145/31/2019
15. Intermediate COCOMO Model : Equations
(Effort)E = ai (KLOC)bi * EAF
(Development Time)D = ci (E)di
(Staff Size)SS = E/D persons
(Productivity)P = KLOC/E
Where EAF Effort Adjustment Factor
Co- efficients for Intermediate COCOMO
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16. Detailed COCOMO Model
It refine intermediate COCOMO Model.
In detailed cocomo, the whole software is divided into different modules and then we apply
COCOMO in different modules to estimate effort and then sum the effort.
The Development phases :
Plan/ requirements: This is the first phase of the development cycle. The requirement is
analyzed, the product plan is set up and a full product specification is generated. This phase
consumes from 6% to 8% of the effort and 10% to 40% of the development time.
Product Design: The second phase of the COCOMO development cycle is concerned with
the determination of the product architecture and the specification of the subsystem. This
phase requires from 16% to 18% of the nominal effort and can last from 19% to 38% of
the development time.
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17. Detailed COCOMO Model :Development Phases
Programming: The third phase of the COCOMO development cycle is divided into
two sub phases: detailed design and code/unit test. This phase requires from 48% to
68% of the effort and lasts from 24% to 64% of the development time.
Integration/test: This phase of the COCOMO development cycle occurs before
delivery. This mainly consist of putting the tested parts together and then testing the
final product this phase requires from 16% to 34% of the nominal effort and can last
from 18% to 34% of the development time
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18. Detailed COCOMO Model :Equations
(Effort)E = ai (KLOC)bi * EAF
(Development Time) D= ci (E)di
Ep = µpE ; µp = Used for effort
Dp = pD ; p = Used for schedule
(staff size )SS = E/D persons
(productivity )P = KLOC/E
EAF = Effort Adjustment factor
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19. Advantages:
COCOMO is realistic and easy to interpret.
Works on historical data and hence is more predictable and accurate.
The drivers are very helpful to understand the impact on the different factors that
affect the project costs.
Disadvantages:
COCOMO model ignores requirements and all documentation.
It ignores customer skills, cooperation, knowledge and other parameters.
It ignores hardware issues.
It is dependent on the amount of time spent in each phase.
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