Jose-Miguel Horcas

Luis Cambelo, Ruben Heradio, Jose-Miguel Horcas et al.

The backbone of a Boolean formula is the set of literals that must be true in every assignment that satisfies the formula. This concept is fundamental to key operations on variability models, including propagating user configuration decisions to identify implied feature selections, detecting dead features and dead code blocks, and preprocessing formulas to accelerate knowledge compilation into tractable representations such as binary decision diagrams. Despite its importance, previous empirical studies have evaluated backbone algorithms solely on SAT competition formulas (typically engineered to test the limits of SAT solvers), leading to inconsistent conclusions about their performance. This study provides the first comprehensive evaluation of formulas derived from real-world variability models, analyzing 21 configurations of 5 state-of-the-art algorithms on 2,371 formulas from configurable systems ranging from 100 variables and 179 clauses to 186,059 variables and 527,240 clauses. The results indicate that variability model formulas are structurally distinct, with higher clause density but greater clause simplicity. Our research provides clear algorithm selection guidelines: Algorithm 2/3 (iterative with solution filtering) is recommended for formulas with 1,000 or fewer variables, while Algorithm 5 (chunked core-based) with adaptive chunk size selection provides the best practical performance for larger formulas. Also, the results show that filtering heuristics have negligible or negative effects on performance for variability models. Finally, the study identifies a research gap: while Algorithm 5 with optimal chunk size can achieve runtime reductions exceeding 50\% compared to Algorithm 2/3 (the one that product line tools implement), the optimal chunk size varies unpredictably across formulas and cannot currently be estimated, opening directions for future research.

Nadia Gamez, Jose-Miguel Horcas, Monica Pinto et al.

With the advent of the Internet of Things (IoT), the percentage of global emissions attributable to Information Systems is expected to further increase in the coming years, due to a proliferation of Internet-connected devices omnipresent in our daily lives (e.g., electric meters, wearable devices, etc.). Although software systems do not directly consume energy, they strongly affect the energy consumption of the hardware. So, developers should be more aware of the energy consumed by these systems during their lifetime, and think about the long-term consequences in the sustainability of our planet Earth. Indeed, once deployed, the energy consumed by a system depends on several factors determined mainly by the usage context. This means that the area of energy-efficient software development needs green development lifecycles that provide appropriate methodologies and tools to identify and analyze the energy hotspots of applications early at design time, and see how they can be self-adapted to the runtime context usage. Regrettably, there is a narrow view of developers and users and their responsibility in the energy consumed during application execution. Developers rarely address energy efficiency as some recent studies show, mostly because they lack appropriate methodologies and tools that help them to produce green software at runtime. So, although software energy efficiency is becoming increasingly important in an ever more technology-dependent world, development processes of self-greening systems supported by tools are still in their infancy. On the other hand, considering that many of current applications are normally deployed in smartphones or in any kind of smart objects (e.g., sensors, watches, etc.), optimizing the energy consumption during the execution will also have a strong impact in battery saving, enhancing the quality of experience of final users.