Surveying the factors that influence maintainability: research design

We want to explore and analyse design decisions that influence maintainability of software. Software maintainability is important because the effort expended on changes and fixes in software is a major cost driver. We take an empirical, qualitative approach, by investigating cases where a change has cost more or less than comparable changes, and analysing the causes for those differences. We will use this analysis of causes as input to following research in which the individual contributions of a selection of those causes will be quantitatively analysed

Evaluating Maintainability Prejudices with a Large-Scale Study of Open-Source Projects

Exaggeration or context changes can render maintainability experience into prejudice. For example, JavaScript is often seen as least elegant language and hence of lowest maintainability. Such prejudice should not guide decisions without prior empirical validation. We formulated 10 hypotheses about maintainability based on prejudices and test them in a large set of open-source projects (6,897 GitHub repositories, 402 million lines, 5 programming languages). We operationalize maintainability with five static analysis metrics. We found that JavaScript code is not worse than other code, Java code shows higher maintainability than C# code and C code has longer methods than other code. The quality of interface documentation is better in Java code than in other code. Code developed by teams is not of higher and large code bases not of lower maintainability. Projects with high maintainability are not more popular or more often forked. Overall, most hypotheses are not supported by open-source data.Comment: 20 page

Software Measurement Activities in Small and Medium Enterprises: an Empirical Assessment

An empirical study for evaluating the proper implementation of measurement/metric programs in software companies in one area of Turkey is presented. The research questions are discussed and validated with the help of senior software managers (more than 15 years’ experience) and then used for interviewing a variety of medium and small scale software companies in Ankara. Observations show that there is a common reluctance/lack of interest in utilizing measurements/metrics despite the fact that they are well known in the industry. A side product of this research is that internationally recognized standards such as ISO and CMMI are pursued if they are a part of project/job requirements; without these requirements, introducing those standards to the companies remains as a long-term target to increase quality

A Quality Model for Actionable Analytics in Rapid Software Development

Background: Accessing relevant data on the product, process, and usage perspectives of software as well as integrating and analyzing such data is crucial for getting reliable and timely actionable insights aimed at continuously managing software quality in Rapid Software Development (RSD). In this context, several software analytics tools have been developed in recent years. However, there is a lack of explainable software analytics that software practitioners trust. Aims: We aimed at creating a quality model (called Q-Rapids quality model) for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies in order to determine relevant metrics as well as product and process factors. We also elicited how these metrics and factors are used and interpreted by practitioners when making decisions in RSD. We specified the Q-Rapids quality model by comparing and integrating the results of the four workshops. Then we implemented the Q-Rapids tool to support the usage of the Q-Rapids quality model as well as the gathering, integration, and analysis of the required data. Afterwards we installed the Q-Rapids tool in the four companies and performed semi-structured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceived the metrics as well as the product and process factors of the Q-Rapids quality model as understandable. Also, they considered the Q-Rapids quality model relevant for identifying product and process deficiencies (e.g., blocking code situations). Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model enables detecting problems that take more time to find manually and adds transparency among the perspectives of system, process, and usage.Comment: This is an Author's Accepted Manuscript of a paper to be published by IEEE in the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018. The final authenticated version will be available onlin

Measuring the Quality of Machine Learning and Optimization Frameworks

Software frameworks are daily and extensively used in research, both for fundamental studies and applications. Researchers usually trust in the quality of these frameworks without any evidence that they are correctly build, indeed they could contain some defects that potentially could affect to thousands of already published and future papers. Considering the important role of these frameworks in the current state-of-the-art in research, their quality should be quantified to show the weaknesses and strengths of each software package. In this paper we study the main static quality properties, defined in the product quality model proposed by the ISO 25010 standard, of ten well-known frameworks. We provide a quality rating for each characteristic depending on the severity of the issues detected in the analysis. In addition, we propose an overall quality rating of 12 levels (ranging from A+ to D-) considering the ratings of all characteristics. As a result, we have data evidence to claim that the analysed frameworks are not in a good shape, because the best overall rating is just a C+ for Mahout framework, i.e., all packages need to go for a revision in the analysed features. Focusing on the characteristics individually, maintainability is by far the one which needs the biggest effort to fix the found defects. On the other hand, performance obtains the best average rating, a result which conforms to our expectations because frameworks’ authors used to take care about how fast their software runs.University of Malaga. Campus de Excelencia Internacional Andalucía Tech. We would like to say thank you to all authors of these frameworks that make research easier for all of us. This research has been partially funded by CELTIC C2017/2-2 in collaboration with companies EMERGYA and SECMOTIC with contracts #8.06/5.47.4997 and #8.06/5.47.4996. It has also been funded by the Spanish Ministry of Science and Innovation and /Junta de Andalucı́a/FEDER under contracts TIN2014-57341-R and TIN2017-88213-R, the network of smart cities CI-RTI (TIN2016-81766-REDT

Measuring the software process and product: Lessons learned in the SEL

The software development process and product can and should be measured. The software measurement process at the Software Engineering Laboratory (SEL) has taught a major lesson: develop a goal-driven paradigm (also characterized as a goal/question/metric paradigm) for data collection. Project analysis under this paradigm leads to a design for evaluating and improving the methodology of software development and maintenance