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Recently, I was reading a paper whose name is "On the Effectiveness of Concern Metrics to Detect Code Smells: An Empirical Study".
I come from a non-English speaking country, and I can not quite understand what Concern Metrics means in the field of software engineering.
It is not referring to the relationship between objects?
I have some understanding of java and c #, some people may be able to use java to give me an example.
Thanks.
Like it is said in the paper's abstract: "While traditional metrics quantify properties of software modules, concern metrics quantify concern properties, such as scattering and tangling." Are you familiar to the cross-cutting concern concept? This question provides examples of concerns: Cross cutting concern example Try to read papers on aspect-oriented programming (AOP) to grasp more concepts in order to understand better the relationship between concerns and code. The metrics are attempts to quantify, for instance, the amount of scatterness of a concern (e.g. login) over the source code.
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this is more of an hypothetical question, but might have great consequences. Lot of us in Modelica community are dealing with large scale systems with expensive simulation times. This is usually not an obstacle for bugfixing and development, but speeding up the simulation might allow for better and faster optimizations.
Recently I came across Modia possibilities, claiming to have superb numerical solvers, achieving better simulation times than Dymola, a state-of-the-art Modelica compiler. The syntax seemed to cover all important bits. Recreating large scale component models in Modia is unfeasible, but what about automatically translating the flattenized Modelica to Modia? Is that realistic? Would that provide a speed up? Has anyone tried before? I have searched for some
This might also hopefully improve integration of Modelica models and postprocesssing / identificaiton tooling within one language, instead of using FMI or invoking a separate executable.
Thanks for any suggestions.
For those interested, we might as well start developing this.
We in the Modia team agrees that the modeling know how in Modelica libraries must be reused. So we are working on a translator (brief details given in https://ep.liu.se/ecp/157/060/ecp19157060.pdf) from Modelica to Modia. The plan is to initially provide translated versions of Modelica.Blocks, Modelica.Electrical.Analog and Modelica.Mechanics together with Modia.
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Recently, I am working on my course project, the topic is the creation of a new hybrid software process model by integrating Scrum and Team Software Process (TSP). Integration of these two models will be based on the SEMAT Essence Kernel Framework.
I am wondering:
Which steps should be followed for this integration (like
determination of the roles and artifacts in these two models)?
What should be the criteria to decide on good sides?
Thanks in advance!
The best way I think I can answer this question is by quoting the agile manifesto.
"Individuals and interactions over processes and tools"
Agile is about people, teamwork and craftsmanship. It's about involving the customer closely to figure out what really is needed - and delivering that, in small increments of working software. Agile is inspect and adapt, based on experimental delivery and the feedback and evidence that comes from that.
Trust yourself. Work closely together and you can do this. The best learning often comes from doing. :)
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What can you do after learning big data related concepts, Hadoop, ML, NLP etc? Where can you implement these?
Not really a software related question - but it's very relevant to current technology and why some software exists. So here is an opinion.
We now live in a world where it is possible to monitor and digitally record information on an epic scale that continues to expand with concepts like The Internet of Things.
With this information it becomes possible to look at the evidence behind decisions that previously would have been made by gut instinct or opinions. What impact does road design have on traffic flow? Which medical drugs get best results in the real world and not just in drugs trials? Is there a correlation between office temperature and productivity? and so on for millions of questions in different domains.
Around the world, organisations are using data they never previously had, to get better at whatever they do (Good or bad).
The big data concepts are the tools for managing all this information. Big Data is not just large in volume, it is often unstructured and in different forms.
So to answer your question. You can implement these concepts by working with organisations that are using Big Data. Hopefully you can see the potential of Big Data along with the mind bending headache it can create when trying to make sense of it.
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I tried googling around. I found one website that talked about how many bugs were found and how much it reduce testing. But it was only one page and I found no papers that talk about it.
Is there anything published to show the time or cost savings of using Ada?
There have been a few studies/papers, the only one that springs immediately is probably the most famous:
Comparing Development Costs of C and Ada (1995)
There is also a presentation:
Programming Languages and Lifecycle Cost (1997)
Other studies:
A Comparison of ADA 83 and C++ (1991)
Also of interest Ironsides, which is [AFAIK] the first verified DNS-server, has a couple of papers that mention the costs of buggy/insecure software and the benefits of formal verification.
I have seen one paper comparing the success rate of student teams in developing real time systems in either Ada or C. In that experiment, success rates were significantly higher in Ada; I'll leave the actual details to the paper.
It is "Software Engineering: On the Right Track" on this page
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R can be very useful for prototyping and some exploratory work. However, I'd like to know what kind of on-line or real-time applications have you (the SO reader) successfully deployed using R and why did you choose R for that task.
Questions:
What kind of problem did it solve? (trending, analysis, etc.)
Why did you choose R over other programming languages? (pros/cons: speed, scaling, etc.)
I'm looking for first-hand experiences, so please do not answer unless you have that.
The breadth of applications is beyond the scope of StackOverflow.
Suppose I said that Java is a fine language for mucking around with APIs, but can anyone point to where it's used in the real world for real deployments? The list would be rather large, with, I kid you not, at least dozens of examples.
If you wish to see examples of real world use, stop by the Bay Area R Users Group, which has had presentations # or by people from Google, Facebook, LinkedIn, Twitter, and more. There are smaller offshoots in NYC, LA, Chicago, London, and elsewhere (there are even Aussies and Kiwis who use R).
So, the best solution to see documented usage is to go to Meetup.com, search for [city] R user group, and identify a Meetup, then select "Past" to see past presentations.