This week we had the possibility to give a webinar about how to work with large scale measurement programs. The webinar was dedicated for everyone who works with software metrics and would like to get more impact from that work.
It is not so much about the numbers, it is about the impact and what the numbers mean. The webinar that we present, provides a good understanding of how to make this impact. Based on our experiences, we chose all one needs to know to implement a measurement program in few weeks rather than years.
The webinar has been recorded and is available at this link: https://www.youtube.com/watch?v=2ChaVT_3djE&feature=youtu.be
Engineers and scientists love to measure. We measure complexity of software, its performance, size and maintainability (just to name a few). We need these measurements in order to construct software, manager organizations or release high quality, high reliable products. However, there is a difference between measuring software aspects and using the measures in decision processes. In this talk, we present the concept of measurement program, measurement system, information quality and indicator-triggered decisions. We show what to consider when setting up measurement programs and provide a hints about the costs and benefits of having the program. We end the talk with presenting recent research results from Software Center, where we combine measurements and machine learning to speed-up software development.
A while back we gave a webinar with a similar title, where we focused on the questions concerning the measurement infrastructure, visualization and assessment of the measurement program. The ACM webinar is presented here:
I’ve written about the ways of assessing how good software is. One of the modern approaches, which I talked about before, is the use of A/B testing and online experiments. Providing the users with different versions of the features/systems/use cases allows the company to understand which of the options provides the best response from the users.
However, there are a number of challenges with this approach – the most prominent being the potential existence of confounding factors. Even if the results show a positive/negative response, we do not really know whether the response is not caused by something else (for example by users being tired, changes in the environment, etc.)
After using GitHub, both as a user and as a researcher, I sometimes wondered whether the star system is actually the right one. I wondered whether we should use a sort-of A/B testing system where we could check how often people usually access certain repositories.
In this paper, the authors take a look at different ways of assessing popularity of repositories. The results show that regardless of the metrics, the popular repositories are popular – i.e. popularity is not dependent of a metric.
Popularity metrics studied:
Total number of downloads of the package
Number of projects dependent on the package
Number of repositories dependent on the package
Source rank of the package
Number of forks
Number of watchers
Number of contributors
Number of stars
Number of open issues
Total number of tags
The actual analysis is quite interesting, so I recommend to take a look at the paper directly.
Working with software requirements and metrics is an important part of research in modern software companies. Although many of the companies are Agile or post-Agile, claiming that they do not have requirements, they still capture user needs in textual forms. For example, they describe user stories, epic, use cases.
This paper is an interesting view on the software requirements quality assessment. Instead of just calculating metrics and creating quality models, they use machine learning to mimic the way in which experts judge what is a good requirement and what is not. They use quality functions, and several of them, to distinguish between the good and bad requirements. Using multiple functions, in a multidimensional space, allows to select groups of requirements that are separated by the other class – the figures in the paper show more how this works in practice.
The summary of the gist of the paper is actually presented best in the introduction (quote): “Summing up, we can compute a set of quantitative metrics of textual requirements, and through them, we can assess the quality of requirements. However, the risk of this approach is to build assessment methods and tools that are both arbitrary in the parameterization of metrics and rigid in the combination of metrics to evaluate the different properties. This is why we propose in this work to develop a flexible assessment method that can be adapted to different contexts, with a high degree of automation. The method consists basically in the emulation of the experts’ judgment on quality through artificial intelligence techniques: first, obtain the expert’s implicit quality function through machine learning, and, second, apply this function to automatically assess the quality of textual requirements.
Our approach to emulate the experts’ judgment, as explained later in detail, is based on well-known machine learning techniques: we have a computer tool learn from a previous human-made classification of requirements according to their quality. Therefore, our work’s intent is not to improve machine learning techniques, but rather to devise a novel application to the field of requirements quality assessment.”
I strongly recommend to read the paper as it provides very good methods to work with requirements quality in many modern organisations.
In the recent years we’ve seen a lot of discussions and good things about cloud computing – sharing platforms (PaaS), services (SaaS) and software thus optimizing the usage of computer resources.
This sharing of resources is important for making the software sustainable, and helps the companies to focus on what their business is about rather than on their IT infrastructure.
Measurement programs are no different – they are often a strategic value for companies, but they are not really something the companies want to spend their R&D budget for (at least not directly). So, how do we make it happen?
Well, we could use the same approach as in SaaS and PaaS and define MaaS (Measurement-as-a-Service) where we can reuse the knowledge across organizations and minimize the cost for working with the software measurement initiatives.
We’ve tried this concept with one of our industrial partners – Ericsson – and it seems that it works very well. You can read more about it in this article.
And the picture below explains a bit how this works.
The method quantified a number of dimensions of a good dashboard and provides a simple set of sliders that can be used to select the right visualization. The companies in the study have found it to be a good input to the understanding of what the stakeholders want when they say “dashboard”.
In the next steps we’re currently working on defining a quality model of KPIs – Key Performance Indicators. The first version has shown that it allows the companies to reduce the number of indicators by as much as 90% by finding the ones which are not of good quality.
In our recent work we have explored the possibility of validating that a measurement program is robust. We have worked with seven companies within the software center to establish a method and evaluate it. The results are presented in a newly accepted paper “MeSRAM – A Method for Assessing Robustness of Measurement Programs in Large Software Development Organizations and Its Industrial Evaluation” to appear in Journal of Systems and Software.
In short the method is based on a collecting the evidence that a measurement program contains elements which are important for the program to be able to handle changes. For example whether a measurement program has a dedicated organization working with it and whether the entire company is able to utilize the results from the measurement program.
The method is similar to the stress-testing of banks, so popular in the last decade.
The next step in our research is finding out which metrics the companies should use to assure the long-term robustness of the measurement program. stay tuned!
When working with companies in different projects I often get a question which metrics should an Agile software development team use. The answer is of course – It depends what your team does… and then a set of questions from my side follow. These questions are designed to make me understand about the activities which the team does, the activities downstream of the process, the product, the process, etc.
I’ve recently looked into this article where the authors make a review of metrics used in agile teams. Although I’ve had high hopes for them, I got a bit disappointed – they were more or less the same metrics as any other team uses.
Staron, Miroslaw, Wilhelm Meding, and Klas Palm. “Release Readiness Indicator for Mature Agile and Lean Software Development Projects.” Agile Processes in Software Engineering and Extreme Programming. Springer Berlin Heidelberg, 2012. 93-107.
The empirical evaluation includes 27 open source projects and 6 industry projects. It’s great that there is an increased body of evidence combining both the open source and the industrial projects. Especially that the results seem to be consistent.