Activity Report 2012. Project-Team RMOD. Analyses and Languages Constructs for Object-Oriented Application Evolution

Activity Report 2012 Project-Team RMOD Analyses and Languages Constructs for Object-Oriented Application Evolutio

Visualizing the customization endeavor in product-based-evolving software product lines: a case of action design research

[EN] Software Product Lines (SPLs) aim at systematically reusing software assets, and deriving products (a.k.a., variants) out of those assets. However, it is not always possible to handle SPL evolution directly through these reusable assets. Time-to-market pressure, expedited bug fixes, or product specifics lead to the evolution to first happen at the product level, and to be later merged back into the SPL platform where the core assets reside. This is referred to as product-based evolution. In this scenario, deciding when and what should go into the next SPL release is far from trivial. Distinct questions arise. How much effort are developers spending on product customization? Which are the most customized core assets? To which extent is the core asset code being reused for a given product? We refer to this endeavor as Customization Analysis, i.e., understanding the functional increments in adjusting products from the last SPL platform release. The scale of the SPLs' code-base calls for customization analysis to be conducted through Visual Analytics tools. This work addresses the design principles for such tools through a joint effort between academia and industry, specifically, Danfoss Drives, a company division in charge of the P400 SPL. Accordingly, we adopt an Action Design Research approach where answers are sought by interacting with the practitioners in the studied situations. We contribute by providing informed goals for customization analysis as well as an intervention in terms of a visual analytics tool. We conclude by discussing to what extent this experience can be generalized to product-based evolving SPL organizations other than Danfoss Drives.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is supported by the Spanish Ministry of Science, Innovation and Universities grant number RTI2018099818-B-I00 and MCIU-AEI TIN2017-90644-REDT (TASOVA). ONEKIN enjoys support from the program 'Grupos de Investigacion del Sistema Univesitario Vasco 2019-2021' under contract IT1235-19. Raul Medeiros enjoys a doctoral grant from the Spanish Ministry of Science and Innovation

Machine Learning-based Test Selection for Simulation-based Testing of Self-driving Cars Software

Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational test cases. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test cases. Past results on software testing optimization have shown that not all the test cases contribute equally to establishing confidence in test subjects' quality and reliability, and the execution of "safe and uninformative" test cases can be skipped to reduce testing effort. However, this problem is only partially addressed in the context of SDC simulation platforms. In this paper, we investigate test selection strategies to increase the cost-effectiveness of simulation-based testing in the context of SDCs. We propose an approach called SDC-Scissor (SDC coSt-effeCtIve teSt SelectOR) that leverages Machine Learning (ML) strategies to identify and skip test cases that are unlikely to detect faults in SDCs before executing them. Our evaluation shows that SDC-Scissor outperforms the baselines. With the Logistic model, we achieve an accuracy of 70%, a precision of 65%, and a recall of 80% in selecting tests leading to a fault and improved testing cost-effectiveness. Specifically, SDC-Scissor avoided the execution of 50% of unnecessary tests as well as outperformed two baseline strategies. Complementary to existing work, we also integrated SDC-Scissor into the context of an industrial organization in the automotive domain to demonstrate how it can be used in industrial settings.Comment: arXiv admin note: substantial text overlap with arXiv:2111.0466

SapFix: Automated End-To-End Repair at Scale

We report our experience with SapFix: the first deployment of automated end-to-end fault fixing, from test case design through to deployed repairs in production code. We have used SapFix at Facebook to repair 6 production systems, each consisting of tens of millions of lines of code, and which are collectively used by hundreds of millions of people worldwide

DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning

Computer programs written in one language are often required to be ported to other languages to support multiple devices and environments. When programs use language specific APIs (Application Programming Interfaces), it is very challenging to migrate these APIs to the corresponding APIs written in other languages. Existing approaches mine API mappings from projects that have corresponding versions in two languages. They rely on the sparse availability of bilingual projects, thus producing a limited number of API mappings. In this paper, we propose an intelligent system called DeepAM for automatically mining API mappings from a large-scale code corpus without bilingual projects. The key component of DeepAM is based on the multi-modal sequence to sequence learning architecture that aims to learn joint semantic representations of bilingual API sequences from big source code data. Experimental results indicate that DeepAM significantly increases the accuracy of API mappings as well as the number of API mappings when compared with the state-of-the-art approaches

Characterizing and Diagnosing Architectural Degeneration of Software Systems from Defect Perspective

The architecture of a software system is known to degrade as the system evolves over time due to change upon change, a phenomenon that is termed architectural degeneration. Previous research has focused largely on structural deviations of an architecture from its baseline. However, another angle to observe architectural degeneration is software defects, especially those that are architecturally related. Such an angle has not been scientifically explored until now. Here, we ask two relevant questions: (1) What do defects indicate about architectural degeneration? and (2) How can architectural degeneration be diagnosed from the defect perspective? To answer question (1), we conducted an exploratory case study analyzing defect data over six releases of a large legacy system (of size approximately 20 million source lines of code and age over 20 years). The relevant defects here are those that span multiple components in the system (called multiple-component defects - MCDs). This case study found that MCDs require more changes to fix and are more persistent across development phases and releases than other types of defects. To answer question (2), we developed an approach (called Diagnosing Architectural Degeneration - DAD) from the defect perspective, and validated it in another, confirmatory, case study involving three releases of a commercial system (of size over 1.5 million source lines of code and age over 13 years). This case study found that components of the system tend to persistently have an impact on architectural degeneration over releases. Especially, such impact of a few components is substantially greater than that of other components. These results are new and they add to the current knowledge on architectural degeneration. The key conclusions from these results are: (i) analysis of MCDs is a viable approach to characterizing architectural degeneration; and (ii) a method such as DAD can be developed for diagnosing architectural degeneration

Supporting the grow-and-prune model for evolving software product lines

207 p.Software Product Lines (SPLs) aim at supporting the development of a whole family of software products through a systematic reuse of shared assets. To this end, SPL development is separated into two interrelated processes: (1) domain engineering (DE), where the scope and variability of the system is defined and reusable core-assets are developed; and (2) application engineering (AE), where products are derived by selecting core assets and resolving variability. Evolution in SPLs is considered to be more challenging than in traditional systems, as both core-assets and products need to co-evolve. The so-called grow-and-prune model has proven great flexibility to incrementally evolve an SPL by letting the products grow, and later prune the product functionalities deemed useful by refactoring and merging them back to the reusable SPL core-asset base. This Thesis aims at supporting the grow-and-prune model as for initiating and enacting the pruning. Initiating the pruning requires SPL engineers to conduct customization analysis, i.e. analyzing how products have changed the core-assets. Customization analysis aims at identifying interesting product customizations to be ported to the core-asset base. However, existing tools do not fulfill engineers needs to conduct this practice. To address this issue, this Thesis elaborates on the SPL engineers' needs when conducting customization analysis, and proposes a data-warehouse approach to help SPL engineers on the analysis. Once the interesting customizations have been identified, the pruning needs to be enacted. This means that product code needs to be ported to the core-asset realm, while products are upgraded with newer functionalities and bug-fixes available in newer core-asset releases. Herein, synchronizing both parties through sync paths is required. However, the state of-the-art tools are not tailored to SPL sync paths, and this hinders synchronizing core-assets and products. To address this issue, this Thesis proposes to leverage existing Version Control Systems (i.e. git/Github) to provide sync operations as first-class construct

Machine learning-based test selection for simulation-based testing of self-driving cars software

Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than eld operational test cases. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test cases. Past results on software testing optimization have shown that not all the test cases contribute equally to establishing con dence in test subjects' quality and reliability, and the execution of \safe and uninformative" test cases can be skipped to reduce testing effort. However, this problem is only partially addressed in the context of SDC simulation platforms. In this paper, we investigate test selection strategies to increase the cost-effectiveness of simulation-based testing in the context of SDCs. We propose an approach called SDC-Scissor (SDC coSt-effeCtIve teSt SelectOR) that leverages Machine Learning (ML) strategies to identify and skip test cases that are unlikely to detect faults in SDCs before executing them