If a tool can automatically refactor our code – is it good or bad for us, programmers?


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Recently, I’ve read an article in Empirical Software Engineering about automated code refactoring. I must admit that I do refactoring quite seldom. It’s a tedious task and for the software that I write, quite unnecessary. My software is often a set of scripts to solve a specific task and then the key is to document it, not refactor. A good documentation helps me to understand what I did in that code and how it works. Yes, I know it sounds like a cliché, but that’s how it is for me. I’m switching tasks so often that I forget what the code was doing.

Nevertheless, I recognize the code that is nicely written, formatted and refactored. Therefore, I was on a lookout for a tool that could do something like that for me – suggest a refactoring that I could implement.

So, this is a paper that I found, which I would like to try out. It is a tool that was evaluated through interviews with designers and developers. Although they can recognize that the code was refactored, but they seemed to be happy about it. So, I’m off to try out the tool:)

Abstract: Refactoring is a maintenance activity that aims to improve design quality while preserving the behavior of a system. Several (semi)automated approaches have been proposed to support developers in this maintenance activity, based on the correction of anti-patterns, which are “poor” solutions to recurring design problems. However, little quantitative evidence exists about the impact of automatically refactored code on program comprehension, and in which context automated refactoring can be as effective as manual refactoring. Leveraging RePOR, an automated refactoring approach based on partial order reduction techniques, we performed an empirical study to investigate whether automated refactoring code structure affects the understandability of systems during comprehension tasks. (1) We surveyed 80 developers, asking them to identify from a set of 20 refactoring changes if they were generated by developers or by a tool, and to rate the refactoring changes according to their design quality; (2) we asked 30 developers to complete code comprehension tasks on 10 systems that were refactored by either a freelancer or an automated refactoring tool. To make comparison fair, for a subset of refactoring actions that introduce new code entities, only synthetic identifiers were presented to practitioners. We measured developers’ performance using the NASA task load index for their effort, the time that they spent performing the tasks, and their percentages of correct answers. Our findings, despite current technology limitations, show that it is reasonable to expect a refactoring tools to match developer code. Indeed, results show that for 3 out of the 5 anti-pattern types studied, developers could not recognize the origin of the refactoring (i.e., whether it was performed by a human or an automatic tool). We also observed that developers do not prefer human refactorings over automated refactorings, except when refactoring Blob classes; and that there is no statistically significant difference between the impact on code understandability of human refactorings and automated refactorings. We conclude that automated refactorings can be as effective as manual refactorings. However, for complex anti-patterns types like the Blob, the perceived quality achieved by developers is slightly higher.

PHANTOM – finding well engineered software projects, fast…


Image by 2427999 from Pixabay

I’ve worked with two great students – Peter and Joshua – who wanted to do something interesting. They developed a tool that could replicate a study from other researchers. However, they did it faster and with less data. We also managed to team up with Mirek from Poznan who improved the classification algorithm and asked his colleagues from new, industrial data.

And this is the outcome – a tool that can connect to a git repository and recognise whether your project is well engineered or not. It helps companies to understand whether their teams are working in a structured manner or ad-hoc.

The tool provides the possibility to assess whether a specific repository is in need for maintenance or not.


Context: Within the field of Mining Software Repositories, there are numerous methods employed to filter datasets in order to avoid analysing low-quality projects. Unfortunately, the existing filtering methods have not kept up with the growth of existing data sources, such as GitHub, and researchers often rely on quick and dirty techniques to curate datasets.

Objective: The objective of this study is to develop a method capable of filtering large quantities of software projects in a resource-efficient way.

Method: This study follows the Design Science Research (DSR) methodology. The proposed method, PHANTOM, extracts five measures from Git logs. Each measure is transformed into a time-series, which is represented as a feature vector for clustering using the k-means algorithm.

Results: Using the ground truth from a previous study, PHANTOM was shown to be able to rediscover the ground truth on the training dataset, and was able to identify “engineered” projects with up to 0.87 Precision and 0.94 Recall on the validation dataset. PHANTOM downloaded and processed the metadata of 1,786,601 GitHub repositories in 21.5 days using a single personal computer, which is over 33% faster than the previous study which used a computer cluster of 200 nodes. The possibility of applying the method outside of the open-source community was investigated by curating 100 repositories owned by two companies.

Conclusions: It is possible to use an unsupervised approach to identify engineered projects. PHANTOM was shown to be competitive compared to the existing supervised approaches while reducing the hardware requirements by two orders of magnitude.

What do elite software developers do in software projects?


Image by Jose B. Garcia Fernandez from Pixabay

A while back I read an article in ZDNet about Linus Torvalds, the creator of Linux, and his daily work. He was (at the time of reading, which is ca. 2 years back) still working on the code. However, he was mostly working on the design of the system, reviewing patches and supporting younger designers. I’ve also read a number of articles which claimed the importance of code reviews as a way of teaching younger designers about the product and the code base.

In this paper, I’ve found that the support for younger designers is what the elite developers do a lot of. It seems that the communication, organisation and support are the activities that the elite developers find important. It’s aligned with what we do at the universities as well. The most elite professors work with students, show them how to program and how to structure their code. Seems like this is a very good way of continuing your career – help other be better.

I guess it’s time to change my wallpaper from “coding” to “teaching”….

Abstract: Open source developers, particularly the elite developers who own the administrative privileges for a project, maintain a diverse portfolio of contributing activities. They not only commit source code but also exert significant efforts on other communicative, organizational, and supportive activities. However, almost all prior research focuses on specific activities and fails to analyze elite developers’ activities in a comprehensive way. To bridge this gap, we conduct an empirical study with fine-grained event data from 20 large open source projects hosted on GITHUB. We investigate elite developers’ contributing activities and their impacts on project outcomes. Our analyses reveal three key findings: (1) elite developers participate in a variety of activities, of which technical contributions (e.g., coding) only account for a small proportion; (2) as the project grows, elite developers tend to put more effort into supportive and communicative activities and less effort into coding; and (3) elite developers’ efforts in nontechnical activities are negatively correlated with the project’s outcomes in terms of productivity and quality in general, except for a positive correlation with the bug fix rate (a quality indicator). These results provide an integrated view of elite developers’ activities and can inform an individual’s decision making about effort allocation, which could lead to improved project outcomes. The results also provide implications for supporting these elite developers.

Can we predict quality of service level quality based on metrics?

link to paper: https://doi.org/10.1016/j.infsof.2020.106313

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Understanding how your product performs in the field is a very hot topic. To be honest, it’s always been. When we design and develop our product, we often want to know whether the outcome is going to be good or not. Well, this is not an easy task because we cannot really know to runtime properties of our products before very close to the final version. It’s difficult to measure end-to-end response time if we only have half of the database, or if we cannot simulate the full load of the product.

In this paper, the authors analysed over 700 web services and measured their code quality and interface quality as predictors for the quality of service.

From the abstract: source code and interface quality metrics/antipatterns are correlated with web service quality attributes (response time, availability, throughput, successability, reliability, compliance, best practices, latency, and documentation).

The internal code/interface quality measures were:

  • NPT: Number of port types, Interface
  • NOPT: Average number of operations in port types, Interface
  • NBS: Number of services, Interface
  • NIPT: Number of identical port types, Interface
  • NIOP: Number of identical operations, Interface
  • ALPS: Average length of port-types signature, Interface
  • AMTO: Average meaningful terms in operation names, Interface
  • AMTM: Average meaningful terms in message names, Interface
  • AMTMP: Average meaningful terms in message parts, Interface
  • AMTP: Average meaningful terms in port-type names, Interface
  • NOD: Number of operations declared, Code
  • NAOD: Number of accessor operations declared, Code
  • ANIPO: Average number of input parameters in operations, Code
  • ANOPO: Average number of output parameters in operations, Code
  • NOM: Number of messages, Code
  • NBE: Number of elements of the schemas, Code
  • NCT: Number of complex types, Code
  • NST: Number of primitive types, Code
  • NBB: Number of bindings, Code
  • NPM: Number of parts per message, Code
  • COH Cohesion: The degree of the functional relatedness of the operations of the service, Code
  • COU Coupling: A measure of the extent to which inter-dependencies exist between the service modules, Code
  • ALOS: Average length of operations signature, Code
  • ALMS: Average length of message signature, Code

This is quite a collection of measures and they are quite interesting, e.g. the average meaningful terms in port-type names. I must admit that it’s a new measure that I’ve not seen before.

The measures of the quality of service were:

  • Response Time: Time taken to send a request and receive a response, QoS
  • Availability: How often is the service available for consumption, QoS
  • Throughput: Total Number of invocations for a given period of time, QoS
  • Successability: Number of response / number of request messages, QoS
  • Reliability: Ratio of the number of error messages to total messages, QoS
  • Compliance: The extent to which a WSDL document follows WSDL specification, QoS
  • Best Practices: The extent to which a web service follows WS-I Basic Profile, QoS
  • Latency: Time taken for the server to process a given request, QoS
  • Documentation: Measure of documentation (i.e. description tags) in WSDL, QoS

Some of these are also quite interesting, e.g. Successability, which is the number of response messages per request messages.

The authors also measured some anti patterns of service design, which they list in the paper. I will not go through these anti-patterns, but I think that they are also correlated with some of the metrics.

I suggest this reading to everyone interested in web services and service design. Perhaps this could help us to get more high-performance services. I hope that I can see more of this type of research in the domain of service security – that’s an interesting area in itself.

Your code and AI – more than precision and recall!

Image by Daniel Hannah from Pixabay

Using machine learning and AI to improve your coding is an important area of research. Together with colleagues we work with these techniques, to take them from open source to more industry quality.

There are two great tools that one can use today already. One of the tools is a beta version of add-in for visual studio, which helps software engineers to write code.


Microsoft is very active in this area and even has release a set of tools that support the development of AI systems: https://www.microsoft.com/en-us/research/project/visual-studio-code-tools-ai/



What is great is that the tools are, naturally, available freely!

Another tool is a DeepCode, which analyzes software code and provides suggestions to improve it – e.g. use a specific design pattern or refactoring.


This is great that we have increasingly more tools and that AI engineering matures. We do not want to have precision and recall steer our development. We want to have real testing and real systems. We also need to work with data quality in order to ensure that the systems are reliable.

The alternative is that we use MCC, precision, recall, F1-score to tell us how good a system is, which is not entirely true. These measures do not provide any view on how the system reflects the requirements put on it. These measures allow us to compare different classifiers, but not systems.

I hope that we can focus more discussion on AI quality and not classification quality/accuracy.