Giovanni Grano

Bio: Giovanni Grano is an academic researcher from University of Zurich. The author has contributed to research in topics: Test case & Unit testing. The author has an hindex of 10, co-authored 18 publications receiving 245 citations. Previous affiliations of Giovanni Grano include University of Salerno.

Android apps and user feedback: a dataset for software evolution and quality improvement

Abstract: Nowadays, Android represents the most popular mobile platform with a market share of around 80%. Previous research showed that data contained in user reviews and code change history of mobile apps represent a rich source of information for reducing software maintenance and development effort, increasing customers' satisfaction. Stemming from this observation, we present in this paper a large dataset of Android applications belonging to 23 different apps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews and more than 450,000 user feedback (extracted with specific text mining approaches). Furthermore, for each app version in our dataset, we employed the Paprika tool and developed several Python scripts to detect 8 different code smells and compute 22 code quality indicators. The paper discusses the potential usefulness of the dataset for future research in the field.

Exploring the integration of user feedback in automated testing of Android applications

Abstract: The intense competition characterizing mobile application's marketplaces forces developers to create and maintain high-quality mobile apps in order to ensure their commercial success and acquire new users. This motivated the research community to propose solutions that automate the testing process of mobile apps. However, the main problem of current testing tools is that they generate redundant and random inputs that are insufficient to properly simulate the human behavior, thus leaving feature and crash bugs undetected until they are encountered by users. To cope with this problem, we conjecture that information available in user reviews—that previous work showed as effective for maintenance and evolution problems—can be successfully exploited to identify the main issues users experience while using mobile applications, e.g., GUI problems and crashes. In this paper we provide initial insights into this direction, investigating (i) what type of user feedback can be actually exploited for testing purposes, (ii) how complementary user feedback and automated testing tools are, when detecting crash bugs or errors and (iii) whether an automated system able to monitor crash-related information reported in user feedback is sufficiently accurate. Results of our study, involving 11,296 reviews of 8 mobile applications, show that user feedback can be exploited to provide contextual details about errors or exceptions detected by automated testing tools. Moreover, they also help detecting bugs that would remain uncovered when rely on testing tools only. Finally, the accuracy of the proposed automated monitoring system demonstrates the feasibility of our vision, i.e., integrate user feedback into testing process.

An empirical investigation on the readability of manual and generated test cases

Abstract: Software testing is one of the most crucial tasks in the typical development process. Developers are usually required to write unit test cases for the code they implement. Since this is a time-consuming task, in last years many approaches and tools for automatic test case generation --- such as EvoSuite--- have been introduced. Nevertheless, developers have to maintain and evolve tests to sustain the changes in the source code; therefore, having readable test cases is important to ease such a process. However, it is still not clear whether developers make an effort in writing readable unit tests. Therefore, in this paper, we conduct an explorative study comparing the readability of manually written test cases with the classes they test. Moreover, we deepen such analysis looking at the readability of automatically generated test cases. Our results suggest that developers tend to neglect the readability of test cases and that automatically generated test cases are generally even less readable than manually written ones.

Test Driven Development: By Example

Abstract: From the Book: “Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons: Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). In Test-Driven Development, you: Write new code only if you first have a failing automated test.Eliminate duplication. Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy The two rules imply an order to the tasks ofprogramming: 1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first 2. Green—make the test work quickly, committing whatever sins necessary in the process 3. Refactor—eliminate all the duplication created in just getting the test to work Red/green/refactor. The TDD’s mantra. Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications: If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage. Courage Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects: Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and: Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet. The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test. Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately. That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress. There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code. Once you are finished reading this book, you should be ready to: Start simplyWrite automated testsRefactor to add design decisions one at a time This book is organized into three sections. An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples. I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference. Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read. A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

A GQM-based Method and a Bayesian Approach for the Detection of Code and Design Smells

Abstract: The presence of code and design smells can have a severe impact on the quality of a program. Consequently, their detection and correction have drawn the attention of both researchers and practitioners who have proposed various approaches to detect code and design smells in programs. However, none of these approaches handle the inherent uncertainty of the detection process. We propose a Bayesian approach to manage this uncertainty. First, we present a systematic process to convert existing state-of-the-art detection rules into a probabilistic model. We illustrate this process by generating a model to detect occurrences of the Blob antipattern. Second, we present results of the validation of the model: we built this model on two open-source programs, GanttProject v1.10.2 and Xerces v2.7.0, and measured its accuracy. Third, we compare our model with another approach to show that it returns the same candidate classes while ordering them to minimise the quality analysts' effort. Finally, we show that when past detection results are available, our model can be calibrated using machine learning techniques to offer an improved, context-specific detection.

Machine Learning Applied to Software Testing: A Systematic Mapping Study

Abstract: Software testing involves probing into the behavior of software systems to uncover faults. Most testing activities are complex and costly, so a practical strategy that has been adopted to circumvent these issues is to automate software testing. There has been a growing interest in applying machine learning (ML) to automate various software engineering activities, including testing-related ones. In this paper, we set out to review the state-of-the art of how ML has been explored to automate and streamline software testing and provide an overview of the research at the intersection of these two fields by conducting a systematic mapping study. We selected 48 primary studies. These selected studies were then categorized according to study type, testing activity, and ML algorithm employed to automate the testing activity. The results highlight the most widely used ML algorithms and identify several avenues for future research. We found that ML algorithms have been used mainly for test-case generation, refinement, and evaluation. Also, ML has been used to evaluate test oracle construction and to predict the cost of testing-related activities. The results of this paper outline the ML algorithms that are most commonly used to automate software-testing activities, helping researchers to understand the current state of research concerning ML applied to software testing. We also found that there is a need for better empirical studies examining how ML algorithms have been used to automate software-testing activities.

An Empirical Evaluation of Evolutionary Algorithms for Unit Test Suite Generation

Abstract: Evolutionary algorithms have been shown to be effective at generating unit test suites optimised for code coverage. While many aspects of these algorithms have been evaluated in detail (e.g., test length and different kinds of techniques aimed at improving performance, like seeding), the influence of the specific algorithms has to date seen less attention in the literature. As it is theoretically impossible to design an algorithm that is best on all possible problems, a common approach in software engineering problems is to first try a Genetic Algorithm, and only afterwards try to refine it or compare it with other algorithms to see if any of them is more suited for the addressed problem. This is particularly important in test generation, since recent work suggests that random search may in practice be equally effective, whereas the reformulation as a many-objective problem seems to be more effective. To shed light on the influence of the search algorithms, we empirically evaluate six different algorithms on a selection of non-trivial open source classes. Our study shows that the use of a test archive makes evolutionary algorithms clearly better than random testing, and it confirms that the many-objective search is the most effective.

The impact of test case summaries on bug fixing performance: An empirical investigation.

Abstract: Automated test generation tools have been widely investigated with the goal of reducing the cost of testing activities. However, generated tests have been shown not to help developers in detecting and finding more bugs even though they reach higher structural coverage compared to manual testing. The main reason is that generated tests are difficult to understand and maintain. Our paper proposes an approach, coined TestDescriber, which automatically generates test case summaries of the portion of code exercised by each individual test, thereby improving understandability. We argue that this approach can complement the current techniques around automated unit test generation or search-based techniques designed to generate a possibly minimal set of test cases. In evaluating our approach we found that (1) developers find twice as many bugs, and (2) test case summaries significantly improve the comprehensibility of test cases, which is considered particularly useful by developers.